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The capacity of an LSTM network can be increased by widening and adding layers.However, usually the former introduces additional parameters, while the latter increases the runtime.

As an alternative we propose the Tensorized LSTM in which the hidden states are represented by tensors and updated via a cross-layer convolution.

By increasing the tensor size, the network can be widened efficiently without additional parameters since the parameters are shared across different locations in the tensor; by delaying the output, the network can be deepened implicitly with little additional runtime since deep computations for each timestep are merged into temporal computations of the sequence.

Experiments conducted on five challenging sequence learning tasks show the potential of the proposed model.

~the number of nodes in the Ising model.

We show that our results are optimal up to logarithmic factors in the dimension.

We obtain our results by extending and strengthening the exchangeable-pairs approach used to prove concentration of measure in this setting by Chatterjee.

We demonstrate the efficacy of such functions as statistics for testing the strength of interactions in social networks in both synthetic and real world data.

This architecture is built upon deep auto-encoders, which non-linearly map the input data into a latent space.

Our key idea is to introduce a novel self-expressive layer between the encoder and the decoder to mimic the ""self-expressiveness"" property that has proven effective in traditional subspace clustering.

Being differentiable, our new self-expressive layer provides a simple but effective way to learn pairwise affinities between all data points through a standard back-propagation procedure.

Being nonlinear, our neural-network based method is able to cluster data points having complex often nonlinear structures.

We further propose pre-training and fine-tuning strategies that let us effectively learn the parameters of our subspace clustering networks.

Our experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised subspace clustering methods.

Our proposed attention module can be trained with or without extra supervision, and gives a sizable boost in accuracy while keeping the network size and computational cost nearly the same.

It leads to significant improvements over state of the art base architecture on three standard action recognition benchmarks across still images and videos, and establishes new state of the art on MPII 12.

We also perform an extensive analysis of our attention module both empirically and analytically.

In terms of the latter, we introduce a novel derivation of bottom-up and top-down attention as low-rank approximations of bilinear pooling methods typically used for fine-grained classification.

From this perspective, our attention formulation suggests a novel characterization of action recognition as a fine-grained recognition problem.

We present finite sample statistical consistency guarantees for Quick Shift on mode and cluster recovery under mild distributional assumptions.

We then apply our results to construct a consistent modal regression algorithm.

Yet, despite their practical success, support for nonsmooth objectives is still lacking, making them unsuitable for many problems of interest in machine learning, such as the Lasso, group Lasso or empirical risk minimization with convex constraints.

In this work, we propose and analyze ProxASAGA, a fully asynchronous sparse method inspired by SAGA, a variance reduced incremental gradient algorithm.

The proposed method is easy to implement and significantly outperforms the state of the art on several nonsmooth, large-scale problems.

We prove that our method achieves a theoretical linear speedup with respect to the sequential version under assumptions on the sparsity of gradients and block-separability of the proximal term.

Empirical benchmarks on a multi-core architecture illustrate practical speedups of up to 12x on a 20-core machine.

However, learning from synthetic faces may not achieve the desired performance due to the discrepancy between distributions of the synthetic and real face images.

To narrow this gap, we propose a Dual-Agent Generative Adversarial Network DA-GAN model, which can improve the realism of a face simulator's output using unlabeled real faces, while preserving the identity information during the realism refinement.

The dual agents are specifically designed for distinguishing real v.

In particular, we employ an off-the-shelf 3D face model as a simulator to generate profile face images with varying poses.

DA-GAN leverages a fully convolutional network as the generator to generate high-resolution images and an auto-encoder as the discriminator with the dual agents.

Besides the novel architecture, we make several key modifications to the standard GAN to preserve pose and texture, preserve identity and stabilize training process: i a pose perception loss; ii an identity perception loss; iii an adversarial loss with a boundary equilibrium regularization term.

Experimental results show that DA-GAN not only presents compelling perceptual results but also significantly outperforms state-of-the-arts on the large-scale and challenging NIST IJB-A unconstrained face recognition benchmark.

In addition, the proposed DA-GAN is also promising as a new approach for solving generic transfer learning problems more effectively.

There are three major challenges: 1 complex dependencies, 2 vanishing and exploding gradients, and 3 efficient parallelization.

In this paper, we introduce a simple yet effective RNN connection structure, the DilatedRNN, which simultaneously tackles all of these challenges.

The proposed architecture is characterized by multi-resolution dilated recurrent skip connections and can be combined flexibly with diverse RNN cells.

Moreover, the DilatedRNN reduces the number of parameters needed and enhances training efficiency significantly, while matching state-of-the-art performance even with standard RNN cells in tasks involving very long-term dependencies.

To provide a theory-based quantification of the architecture's advantages, we introduce a memory capacity measure, the mean recurrent length, which is more suitable for RNNs with long skip connections than existing measures.

We rigorously prove the advantages of the DilatedRNN over other recurrent neural architectures.

This leads to the discovery of family of graph spectral distances denoted as FGSD and their based graph feature representations, which we prove to possess most of these desired properties.

To both evaluate the quality of graph features produced by FGSD and demonstrate their utility, we apply them to the graph classification problem.

Through extensive experiments, we show that a simple SVM based classification algorithm, driven with our powerful FGSD based graph features, significantly outperforms all the more sophisticated state-of-art algorithms on the unlabeled node datasets in terms of both accuracy and speed; it also yields very competitive results on the labeled datasets - despite the fact it does not utilize any node label information.

However, existing GLBs scale poorly with the number of visit web page and the number of arms, limiting their utility in practice.

This paper proposes new, scalable solutions to the GLB problem in two respects.

As a special case, we apply GLOC to the online Newton step algorithm, which results in a low-regret GLB algorithm with much lower time and memory complexity than prior work.

Such methods can be implemented via hashing algorithms i.

Finally, we propose a fast approximate hash-key computation inner product with a better accuracy than the state-of-the-art, which can be of independent interest.

We conclude the paper with preliminary experimental results confirming the merits of our methods.

The result is a posterior distribution over the integral that explicitly accounts for dual sources of numerical approximation error due to a severely limited computational budget.

This construction is applied to account, in a statistically principled manner, for the impact of numerical errors that at learn more here are confounding factors in functional cardiac model assessment.

So far, distributed machine learning frameworks have largely ignored the possibility of failures, especially arbitrary i.

continue reading of failures include software bugs, network asynchrony, biases in local datasets, as well as attackers trying to compromise the entire system.

We first show that no gradient aggregation rule based on a linear combination of the vectors proposed by the workers i.

We also report on experimental evaluations of Krum.

Sometimes, it is desirable for a human operator to interrupt an agent in order to prevent dangerous situations from happening.

Yet, as part of their learning process, agents may link these interruptions, that impact their reward, to specific states and deliberately avoid them.

The situation is particularly challenging in a multi-agent context because agents might not only learn from their own past interruptions, but also from those of other agents.

Orseau and Armstrong defined safe interruptibility for one learner, but their work does not naturally extend to multi-agent systems.

This paper introduces dynamic safe interruptibility, an alternative definition more suited to decentralized learning problems, and studies this notion in two learning frameworks: joint action learners and independent learners.

We give realistic sufficient conditions on the learning algorithm to enable dynamic safe interruptibility in the case of joint action learners, yet show that these conditions are not sufficient for independent learners.

We show however that if agents ãã©ãã«ãŒãã²ãŒã ã®ããŠã³ããŒãã®PC detect interruptions, it is possible to prune the observations to ensure dynamic safe interruptibility even for independent learners.

In real life situations, however, the utility function is not fully known in advance and can only be estimated via interactions.

For instance, whether a user likes a movie or not can be reliably evaluated only after it was shown to her.

Or, the range of influence of a user in a social network can be estimated only after she is selected to advertise the product.

We model such problems as an interactive submodular bandit optimization, where in each round we receive a context e.

We then receive a noisy feedback about the utility of the action e.

Given a bounded-RKHS norm kernel over the context-action-payoff space that governs the smoothness åŒ·çã®ãããã¯ãè§£é€ããã²ãŒã ããã66 the utility function, SM-UCB keeps an upper-confidence bound on the payoff function that allows it to asymptotically achieve no-regret.

Finally, we evaluate our results on four concrete applications, including movie recommendation on the MovieLense data setnews recommendation on Yahoo!

Webscope datasetinteractive influence maximization on a subset of the Facebook networkand personalized data summarization on Reuters Corpus.

In all these applications, we observe that SM-UCB consistently outperforms the prior art.

At the core of our system is a physical world representation that is first recovered by a perception module and then utilized by physics and graphics engines.

During training, the perception module and the generative models learn by visual de-animation --- interpreting and reconstructing the visual information stream.

During testing, the system first recovers the physical world state, and then uses the generative models for reasoning and future prediction.

Even more so than forward simulation, inverting a physics or graphics engine is a computationally hard problem; we overcome this challenge by using a convolutional inversion network.

Our system quickly recognizes the physical world state from appearance and motion cues, and has the flexibility to incorporate both differentiable https://list-games-promocode.site/1/115.html non-differentiable physics and graphics engines.

We evaluate our system on both synthetic and real datasets involving multiple physical scenes, and demonstrate that our system performs well on both physical state estimation and reasoning problems.

We further show ç¡æã²ãŒã ã³ãŒãã¯ã©ã the knowledge learned on the synthetic dataset generalizes to constrained real images.

Our approach battles domain shift with a domain adversarial loss, and generalizes the embedding to novel task using a metric learning-based approach.

Our model is simultaneously optimized on labeled source data and unlabeled or sparsely labeled data in the target domain.

Our method shows compelling results on novel classes within a new domain even when only a few labeled examples per class are available, outperforming the prevalent fine-tuning approach.

In addition, we demonstrate the effectiveness of our framework on the transfer learning task from image object recognition to video action recognition.

However, since it only searches for local optima at each time step through one-step forward looking, it usually cannot output the best target sentence.

Specifically, we propose a recurrent structure for the value network, and train its parameters from bilingual data.

Experiments show that such an approach can significantly improve the translation accuracy on several translation tasks.

PSM offers significant advantages over other competing methods: 1 PSM naturally obtains the complete solution path for all values of the regularization parameter; 2 PSM provides a high precision dual certificate stopping criterion; 3 PSM yields sparse solutions through very few iterations, and the solution sparsity significantly reduces the computational cost per iteration.

Particularly, we demonstrate the superiority of PSM over various sparse learning approaches, including Dantzig selector for sparse linear regression, sparse support vector machine for sparse linear classification, and sparse differential network estimation.

We then provide sufficient conditions under which PSM always outputs sparse solutions such that its computational performance can be significantly boosted.

Thorough numerical experiments are provided to demonstrate the outstanding performance of the PSM method.

Among them, learning models with grouped variables have shown competitive performance for prediction and variable selection.

However, the previous works mainly focus on the least squares regression problem, not the classification task.

Thus, it is desired to design the new additive classification model with variable selection capability for many real-world applications which focus on high-dimensional data classification.

To address this challenging problem, in this paper, we investigate the classification with group sparse additive models in reproducing kernel Hilbert spaces.

Generalization error bound is derived and proved by integrating the sample error analysis with empirical covering numbers and the hypothesis error estimate with the stepping stone technique.

Our new bound shows that GroupSAM can achieve a satisfactory learning rate with polynomial decay.

Experimental results on synthetic data and seven benchmark datasets consistently show the effectiveness of our new approach.

This is very helpful since inference, or relevant bounds, may be much easier to obtain or more accurate for some model in the class.

Here we introduce methods to extend the approach to models with higher-order potentials and develop theoretical insights.

We demonstrate empirically that rerooting can significantly improve accuracy of methods of inference for higher-order models at negligible computational cost.

We very ã²ãŒã ã¯ã€ãã¢ãŠãããŠã³ããŒã very matrices with complex entries which give significant further accuracy improvement.

We provide geometric and Markov chain-based perspectives to help understand the benefits, and empirical results which suggest that the approach is helpful in a wider range of applications.

In this context, a number of recent studies have focused on defining, detecting, and removing unfairness from data-driven decision systems.

However, the existing notions of fairness, PCçšã®ç¡ææ¥œããã²ãŒã ãããŠã³ããŒã on parity equality in treatment or outcomes for different social groups, tend to be quite stringent, limiting the overall decision making accuracy.

In this paper, we draw inspiration from the fair-division and envy-freeness literature in economics and game theory and propose preference-based notions of fairness -- given the choice between various sets of decision treatments or outcomes, any group of users would collectively prefer its treatment or outcomes, regardless of the dis parity as compared to the other groups.

Then, we introduce tractable proxies to design margin-based classifiers that satisfy these preference-based notions of fairness.

Finally, we experiment with a variety of synthetic and real-world datasets and show that preference-based fairness allows for greater decision accuracy than parity-based fairness.

A popular solution is combining multiple sources of weak supervision using generative models.

The structure of these models affects the quality of the training labels, but is difficult to learn without any ground truth labels.

We instead rely on weak supervision sources having some structure by virtue of being encoded programmatically.

We present Coral, a paradigm that infers generative model structure by statically analyzing the code for these heuristics, thus significantly reducing the amount of data required to learn structure.

We prove that Coral's sample complexity scales quasilinearly with the number of heuristics and number of relations identified, improving over the standard sample complexity, which is exponential in n for learning n-th degree relations.

Empirically, Coral matches or outperforms traditional structure learning approaches by up to 3.

Using Coral to model dependencies instead of assuming independence results in better performance than a fully supervised model by 3.

Here we develop structured exponential family embeddings S-EFEa method for discovering embeddings that vary across related groups of data.

We study how the word usage of U.

Congressional speeches varies across states and party affiliation, how words are used differently across sections of the ArXiv, and how the co-purchase patterns of groceries can vary across seasons.

Key to the success of our method is that the groups share statistical information.

We develop two sharing strategies: hierarchical modeling and amortization.

We demonstrate the benefits of this approach in empirical studies of speeches, abstracts, and shopping baskets.

We show how SEFE enables group-specific interpretation of word usage, and outperforms EFE in predicting held-out data.

We learn the test features that best indicate the differences between observed samples and a reference model, by minimizing the false negative rate.

These features are constructed via Stein's method, meaning that it is not necessary to compute the normalising constant of the model.

We analyse the asymptotic Bahadur efficiency of the new test, and prove that under a mean-shift alternative, our test always has greater relative efficiency than a previous linear-time kernel test, regardless of the choice of parameters for that test.

In experiments, the performance of our read more exceeds that of the earlier linear-time test, and matches or exceeds the power of a quadratic-time kernel test.

In high dimensions and where model structure may be exploited, our goodness of fit test performs far better than a quadratic-time two-sample test based on the Maximum Mean Discrepancy, with samples drawn from the model.

Such stereotyped structure suggests the existence of common computational principles.

However, such principles have remained largely elusive.

Inspired by gated-memory networks, namely long short-term memory networks LSTMswe introduce a recurrent neural network in which information is gated through inhibitory cells that are subtractive subLSTM.

We propose a natural mapping of subLSTMs onto known canonical excitatory-inhibitory cortical microcircuits.

Our empirical evaluation across sequential image classification and language modelling tasks shows that subLSTM units can achieve similar performance to LSTM units.

These results suggest that cortical circuits can be optimised to solve complex contextual problems and proposes a novel view on their computational function.

Overall our work provides a step towards unifying recurrent networks as used in machine learning with their biological counterparts.

We study the norms obtained from extending the k-support norm and OWL norms to the setting in which there are overlapping groups.

The resulting norms are in general NP-hard to compute, but they are tractable for certain collections of groups.

To demonstrate this fact, we develop a dynamic program for the problem of projecting onto the set of vectors supported by a fixed number of groups.

Our dynamic program utilizes tree decompositions and its complexity scales with the treewidth.

This program can be converted to an extended formulation which, for the associated group structure, models the k-group support norms and an overlapping group variant of the ordered weighted l1 norm.

Numerical results demonstrate the efficacy of the new penalties.

We show that while it is sensible to think of recall as simply retrieving items when probed with a cue - typically the item list itself - it is better to think of recognition as retrieving cues when probed with items.

To test this theory, by manipulating the number of items and cues in a memory experiment, we show a crossover effect in memory performance within subjects such that recognition performance is superior to recall performance when the number of items is greater than the number of cues and recall performance is better than recognition when the converse holds.

We build a simple computational model around this theory, using sampling https://list-games-promocode.site/1/1936.html approximate an ideal Bayesian observer encoding and retrieving situational co-occurrence frequencies of stimuli and retrieval cues.

This model robustly reproduces a number of dissociations in recognition and recall previously used to argue for dual-process accounts of declarative memory.

For any task loss, we construct a convex surrogate that can be optimized via stochastic gradient descent and we prove tight bounds on the so-called ""calibration function"" relating the excess surrogate risk to the actual risk.

In contrast to prior related work, we carefully monitor the effect of the exponential number of classes in the learning guarantees as well as on the optimization complexity.

As an interesting consequence, we formalize the intuition that some task losses make learning harder than others, and that the classical 0-1 loss is ill-suited for structured prediction.

The standard training paradigm for these models is maximum likelihood estimation MLEor minimizing the cross-entropy of the human responses.

Across a variety of domains, a recurring problem with MLE trained generative neural dialog models G is that they tend to produce 'safe' and generic responses like ""I don't know"", ""I can't tell"".

In contrast, discriminative dialog models D that are trained to rank a list of candidate human responses outperform their generative counterparts; in terms of automatic metrics, diversity, and informativeness of the responses.

However, D is not useful in practice since it can not be deployed to have real conversations with users.

Our work aims to achieve the best of both worlds -- the practical usefulness Tã¹ããããã«ã G and the strong performance of D -- via knowledge transfer from D to G.

Our primary ã€ããŒã³ã ã²ãŒã ã¯ãã¹ã¯ãŒã is an end-to-end trainable generative visual dialog model, where G receives gradients macbookçšã®ç¡æããŠã³ããŒãã²ãŒã D as a perceptual not adversarial loss of the sequence sampled from G.

We leverage the recently proposed Gumbel-Softmax GS approximation to the discrete distribution -- specifically, a RNN is augmented with a sequence of GS samplers, which coupled with the straight-through gradient estimator enables end-to-end differentiability.

We also introduce a stronger encoder for visual dialog, and employ a self-attention mechanism for answer encoding along with a metric learning loss to aid D in better capturing semantic similarities in answer responses.

Overall, our proposed model outperforms state-of-the-art on the VisDial dataset by a significant margin 2.

To capture the temporal coherence, in this paper, we develop MaskRNN, a recurrent neural net approach which fuses in each frame the output of two deep nets for each object instance - a binary segmentation net providing a mask and a localization net providing a bounding box.

Due to the recurrent component and the localization component, our method is able to take advantage of long-term temporal structures of the video data as well as rejecting outliers.

We validate the proposed algorithm on three challenging benchmark datasets, the DAVIS-2016 dataset, the DAVIS-2017 dataset, and the Segtrack v2 dataset, achieving state-of-the-art performance on all of them.

Inspired by a recently proposed model for general image classification, Recurrent Convolution Neural Network RCNNwe propose a new architecture named Gated RCNN GRCNN for solving this problem.

Its critical component, Gated Recurrent Convolution Layer GRCLis constructed by adding a gate to the Recurrent Convolution Layer RCLthe critical component of RCNN.

The gate controls the context modulation in RCL and balances the feed-forward information and the recurrent information.

In addition, an efficient Bidirectional Long Short-Term Memory BLSTM is built for sequence modeling.

The GRCNN is combined with BLSTM to recognize text in natural images.

The entire GRCNN-BLSTM model can be trained end-to-end.

Experiments show that the proposed model outperforms existing methods on several benchmark datasets including the IIIT-5K, Street View Text SVT and ICDAR.

It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bitwise operations, leading to much faster test-time inference and lower power consumption.

However, previous works on binarizing CNNs usually result in severe prediction accuracy degradation.

In this paper, we address this issue with two major innovations: 1 approximating full-precision weights with the linear combination of multiple binary weight bases; 2 employing multiple binary activations to alleviate information loss.

The implementation of the resulting binary CNN, denoted as ABC-Net, is shown to achieve much closer performance to its full-precision counterpart, and even reach the comparable prediction accuracy on ImageNet and forest trail datasets, given adequate binary weight bases and activations.

As training the CNNs requires sufficiently large ground truth training data, existing approaches resort to synthetic, unrealistic datasets.

On the other hand, unsupervised methods are capable of leveraging real-world videos for training where the ground truth flow fields are not available.

These methods, however, rely on the fundamental assumptions of brightness constancy and spatial smoothness priors which do not hold near motion boundaries.

In this paper, we propose to exploit unlabeled videos for semi-supervised learning of optical flow with a Generative Adversarial Network.

Our key insight is that the adversarial loss can capture the structural patterns of flow warp errors without making explicit assumptions.

Extensive experiments on benchmark datasets demonstrate that the proposed semi-supervised algorithm performs favorably against purely supervised and semi-supervised learning schemes.

In contrast to recent learning based methods for 3D reconstruction, we leverage the underlying 3D geometry of the problem through feature projection and unprojection along viewing rays.

By formulating these operations in a differentiable manner, we are able to learn the system end-to-end for the task of metric 3D reconstruction.

End-to-end learning allows us to jointly reason about shape priors while conforming to geometric constraints, enabling reconstruction from much fewer images even a single image than required by classical approaches as well as completion of unseen surfaces.

We thoroughly evaluate our approach on the ShapeNet dataset and demonstrate the benefits over classical approaches and recent learning based methods.

Our results reveal that noise can make the problem considerably more difficult, with strict increases in the scaling laws even at low noise levels.

Existing feed-forward based methods, while enjoying the inference are iPhoneçšã²ãŒã opinion, are mainly limited by inability of generalizing to unseen styles or compromised visual quality.

In this paper, we present a simple yet effective method that tackles these limitations without training on any pre-defined styles.

The key ingredient of our method is a pair of feature transforms, whitening and coloring, that are embedded to an image reconstruction network.

The whitening and coloring transforms reflect direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer.

We demonstrate the effectiveness of our algorithm by generating high-quality stylized images with comparisons to a number of recent methods.

We also analyze our method by visualizing the whitened features and synthesizing textures by simple feature coloring.

However, we empirically found that the model shrinkage of the EPM does not typically work appropriately and leads to an overfitted solution.

In order to ensure that the model shrinkage effect of the EPM works in an appropriate manner, we proposed two novel generative constructions of the EPM: CEPM incorporating constrained gamma priors, and DEPM incorporating Dirichlet priors instead of the gamma priors.

We experimentally confirmed that the model shrinkage of the proposed models works well and that the IDEPM indicated state-of-the-art performance in generalization ability, link prediction accuracy, mixing efficiency, and convergence speed.

In the first stage the condition image and the target pose are fed into a U-Net-like network to generate an initial but coarse image of the person with the target pose.

The second stage then refines the initial and blurry result by training a U-Net-like generator in an adversarial way.

Popular inference algorithms such as belief propagation BP and generalized belief propagation GBP are intimately related to linear programming LP relaxation within the Sherali-Adams hierarchy.

Despite the popularity of these algorithms, it is well understood that the Sum-of-Squares SOS hierarchy based on semidefinite programming SDP can provide superior guarantees.

In this paper, we propose binary SDP relaxations for MAP inference using the SOS hierarchy with two innovations focused on computational efficiency.

Firstly, in analogy to BP and its variants, we only introduce decision variables corresponding to contiguous regions in the graphical model.

Secondly, we solve the resulting SDP using a non-convex Burer-Monteiro style method, and link a sequential rounding procedure.

We demonstrate that the resulting algorithm can solve problems with tens just click for source thousands of variables within minutes, and outperforms BP and GBP on practical problems such as image denoising and Ising spin glasses.

Finally, for specific graph types, we establish a sufficient condition for the tightness of the proposed partial SOS relaxation.

While practitioners often employ variable importance methods that rely on this impurity-based information, these methods remain poorly characterized from a theoretical perspective.

We provide novel insights into the performance of these methods by deriving finite sample performance guarantees in a high-dimensional setting under various modeling assumptions.

We further demonstrate the effectiveness of these impurity-based ã«ãŒã¬ããã¢ã³ããã€ãã²ãŒã ã®ããŠã³ããŒã via an extensive set of simulations.

The GRU is typically trained using a gradient-based method, which is subject to the exploding gradient problem in which the gradient increases significantly.

This problem is caused by an abrupt change in the dynamics of the GRU due to a small variation in the parameters.

In this paper, we find a condition under which the dynamics of the GRU changes drastically and propose a learning method to address the exploding gradient problem.

Our method constrains the dynamics of the GRU so that it does not drastically change.

We evaluated our method in experiments on language modeling and polyphonic music modeling.

Our experiments showed that our method can prevent the exploding gradient problem and improve modeling accuracy.

This observation leads to many interesting results on general can ã²ãŒã ã¹ãããŒãªã³ã©ã€ã³ for matrix estimation problems: 1.

The approach is elegant but falls short of a full description of the supervised game, and says little about the key player, the generator: for example, what does the generator actually converge to if solving the GAN game means convergence in some space of parameters?

How does that provide hints on the generator's design and compare to the flourishing but almost exclusively experimental literature on the subject?

In this paper, we unveil a broad class ã¢ã³ããã€ãã®ããã®æé«ã®ã¹ããŒãã²ãŒã ãµã€ã distributions for which such convergence happens --- namely, deformed exponential families, a wide superset of exponential families --.

The key to our results is a variational generalization of an old theorem that relates the KL divergence between regular exponential families and divergences between their natural parameters.

We complete this picture with additional results and experimental insights on how these results may be used to ground further improvements of GAN architectures, via i a principled design of the activation functions in the generator and ii an explicit integration of proper composite losses' link function in the discriminator.

In this work, we aim to model a distribution of possible outputs in a conditional generative modeling setting.

The ambiguity of the mapping is distilled in a low-dimensional latent vector, which can be randomly sampled at test time.

A generator learns to map the given input, combined with this latent code, to the output.

We explicitly encourage the connection between output and the latent code to be invertible.

This helps prevent a many-to-one mapping from the latent code to the output during training, also known as the problem of mode collapse, and produces more diverse results.

We explore several variants of this approach by employing different training objectives, network architectures, and methods of injecting the latent code.

Our proposed method encourages bijective consistency between the latent encoding and output modes.

We present a systematic comparison of our method and other variants on both perceptual realism and diversity.

However, our studies show that submatrices with see more ranks could coexist in the same user-item rating matrix, so that approximations with fixed ranks cannot perfectly describe the internal structures of the rating matrix, therefore leading to inferior recommendation accuracy.

In this paper, a mixture-rank matrix approximation MRMA method is proposed, in which user-item ratings can be characterized by a mixture of LRMA models with different ranks.

Meanwhile, a learning algorithm capitalizing on iterated condition modes is proposed to tackle the non-convex optimization problem pertaining to MRMA.

Experimental studies on MovieLens and Netflix datasets demonstrate that MRMA can outperform six state-of-the-art LRMA-based CF methods ãã¹ã¿ãŒã¹ãã³ãã°ã€ã³ terms of recommendation accuracy.

DR-submodularity captures a subclass of non-convex functions that enables both exact minimization and approximate maximization in polynomial time.

In this work we study the problem of maximizing non-monotone DR-submodular continuous functions under general down-closed convex constraints.

We start by investigating geometric properties that underlie such objectives, e.

These properties are then used to devise two optimization algorithms with provable guarantees.

This algorithm allows the use of existing methods for finding approximately stationary points as a subroutine, thus, harnessing recent progress in non-convex optimization.

Finally, we extend our approach to a broader class of generalized DR-submodular continuous functions, which captures a wider spectrum of applications.

Our theoretical findings are validated on synthetic and real-world problem instances.

In this paper, we look in particular at the task of learning a single visual representation that can be successfully utilized in the analysis of very different types of images, from dog breeds to stop signs and digits.

Inspired by recent work on learning networks that predict the parameters of another, we develop a tunable deep network architecture that, by means of adapter residual modules, can be steered on the fly to diverse visual domains.

Our method achieves a high degree of parameter sharing while maintaining or even improving the accuracy of domain-specific representations.

We also introduce the Visual Decathlon Challenge, a benchmark that evaluates the ability of representations to capture simultaneously ten very different visual domains and measures their ability to recognize well uniformly.

We prove that coordinate descent for a regularized regression problem, in which the penalty is a separable sum of support functions, is exactly equivalent to Dykstra's algorithm applied to the dual problem.

ADMM on the dual problem is also seen to be equivalent, in the special case of two sets, with one being a linear subspace.

These connections, aside from being interesting in their own right, suggest new ways of analyzing and extending coordinate ãããããŒã«ã²ãŒã ç¡æ />For example, from existing convergence theory on Dykstra's algorithm over polyhedra, we discern that coordinate descent for the lasso problem converges at an asymptotically linear rate.

We also develop two parallel versions of coordinate descent, based on the Dykstra and ADMM connections.

A naive solution that repeatedly projects the viewing sphere to all tangent planes is accurate, but much too computationally intensive for real problems.

We propose to learn a spherical convolutional network that translates a planar CNN to process 360Â° imagery directly in ãã€ã€ã«ããŒã«ãŒã«ãžã equirectangular projection.

Our approach learns to reproduce the flat filter outputs on 360Â° data, sensitive to the varying distortion effects across the viewing sphere.

The key benefits are 1 efficient feature extraction for 360Â° images and video, and 2 the ability to leverage powerful pre-trained networks researchers have carefully honed together with massive labeled image training sets for perspective images.

Our method yields the most accurate results while saving orders of magnitude in computation versus the existing exact reprojection solution.

This introduces challenge for learning-based approaches, as 3D object annotations in real images are scarce.

Previous work chose to train on synthetic data with ground truth 3D information, but suffered from the domain adaptation issue when tested on real data.

In this work, we propose an end-to-end trainable framework, sequentially estimating 2.

Our disentangled, two-step formulation has three advantages.

First, compared to full 3D shape, 2.

Second, for 3D reconstruction from the 2.

This further relieves the domain adaptation problem.

Third, we derive differentiable projective functions from 3D shape https://list-games-promocode.site/1/1866.html 2.

Our framework achieves state-of-the-art performance on 3D click to see more reconstruction.

The visual question answering VQA problem is an excellent way to test such reasoning capabilities of an AI model and its multimodal representation learning.

However, the current VQA models are over-simplified deep neural networks, comprised of a long short-term memory LSTM unit for question comprehension and a convolutional neural network CNN for learning single image representation.

We argue that the single visual representation contains a limited and general information about the image contents and thus limits the model reasoning capabilities.

In this work we introduce a modular neural network model that learns a multimodal and multifaceted representation of the image and please click for source question.

The proposed model learns to use the multimodal representation to reason about the image entities and achieves a new state-of-the-art performance on both VQA benchmark datasets, VQA v1.

The absolute error is a canonical example.

Many existing methods for this task reduce to binary classification problems and employ surrogate losses, such as the hinge loss.

We instead derive uniquely defined surrogate ordinal regression loss functions by seeking the predictor that is robust to the worst-case approximations of training data labels, subject to matching certain provided training data statistics.

We demonstrate the advantages of our approach over other surrogate losses based on hinge loss approximations using UCI ordinal prediction tasks.

Existing theoretical analysis either only studies specific algorithms or only presents upper bounds on the generalization error but not on the excess risk.

In this paper, we propose a unified algorithm-dependent framework for HTL through a novel notion of transformation functions, which characterizes the relation between the source and the target domains.

We conduct a general risk analysis of this framework and in particular, we show for the first time, if two domains are related, HTL enjoys faster convergence rates of excess risks for Kernel Smoothing and Kernel Ridge Regression than those of the classical non-transfer learning settings.

We accompany this framework with an analysis of cross-validation for HTL to search for the best transfer technique and gracefully reduce to non-transfer learning when HTL is not helpful.

Experiments on robotics and neural imaging data demonstrate the effectiveness of our framework.

In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data.

The representation learning process is formulated as an adversarial minimax game.

We analyze the optimal equilibrium of such a game and find that it amounts to maximizing the uncertainty of inferring the detrimental factor given the representation while maximizing the certainty of making task-specific predictions.

On three benchmark tasks, namely fair and bias-free classification, language-independent generation, and lighting-independent image classification, we show that the proposed framework induces an invariant representation, and leads to better generalization evidenced by the improved performance.

However, formal theoretical understanding of why SGD can train neural networks in practice is largely missing.

In this paper, we make progress on understanding this mystery by providing a convergence analysis for SGD on a rich subset of two-layer feedforward networks with ReLU activations.

This subset is characterized by a special structure called ""identity mapping"".

Unlike normal vanilla networks, the ""identity mapping"" makes our network asymmetric and thus the global minimum is unique.

To complement our theory, we are also able to show experimentally that multi-layer networks with this mapping have better performance compared with normal vanilla networks.

Our convergence theorem differs from traditional non-convex optimization techniques.

Then in phase II, SGD enters a nice one point convex region and converges.

We also show that the identity mapping is necessary for convergence, as it moves the initial point to a better place for optimization.

Experiment verifies our claims.

The use of mini-batches has become a golden standard in the machine learning community, because the mini-batch techniques stabilize the gradient estimate and can easily make good use of parallel computing.

Further, we show that even in non-mini-batch settings, our method achieves the best known convergence rate for non-strongly convex and strongly convex objectives.

In this paper, a novel approach is proposed which divides the training process into two consecutive phases to obtain better generalization performance: Bayesian sampling and stochastic optimization.

These strategies can overcome the challenge of early trapping into bad local minima and have achieved remarkable improvements in various types of neural networks as shown in our theoretical analysis and empirical experiments.

This setting is in particular interesting since it captures natural online extensions of well-studied offline linear optimization problems which are NP-hard, yet admit efficient approximation algorithms.

We present new algorithms with significantly improved oracle complexity for both the full information and bandit variants of the problem.

Numerical results on linear regression and logistic regression with elastic net regularization show that GeoPG compares favorably with Nesterov's accelerated proximal gradient method, especially when the problem is ill-conditioned.

Oja's iteration maintains a running estimate of the true principal component from streaming data and enjoys less temporal and spatial complexities.

We show that the Oja's iteration for the top eigenvector generates a continuous-state discrete-time Markov chain over the unit sphere.

We characterize the Oja's iteration in three phases using diffusion approximation and weak convergence tools.

Our three-phase analysis further provides a finite-sample error bound for the running estimate, which matches the minimax information lower bound for PCA under the additional assumption of bounded samples.

Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome.

While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively.

Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning.

This viewpoint shifts attention from ""What is the right fairness criterion?

First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion.

Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem.

Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them.

As a preliminary step in our analysis, we extend a nonparametric online learning algorithm by Hazan and Megiddo enabling it to compete against functions whose Lipschitzness is measured with respect to an arbitrary Mahalanobis metric.

This paper takes a step forward in this direction and focuses on ensuring machine learning models deliver fair decisions.

In legal scholarships, the notion of fairness itself is evolving and multi-faceted.

We set an overarching goal to develop a unified machine learning framework that is able to handle any definitions of fairness, their combinations, and also new definitions that might be stipulated in the future.

To achieve our goal, we recycle two well-established machine learning techniques, privileged learning and distribution matching, and harmonize them for satisfying multi-faceted fairness definitions.

We consider protected characteristics such as race and gender as privileged information that is available at training but not at test time; this accelerates model training and delivers fairness through unawareness.

Further, we cast demographic parity, equalized odds, and equality of opportunity as a classical two-sample problem of conditional distributions, which can be solved in a general form by using distance measures in Hilbert Space.

We show several existing models are special cases of ours.

Finally, we advocate returning the Pareto frontier of multi-objective minimization of error and unfairness in predictions.

This will facilitate decision makers to select an operating point and to be accountable for it.

Thus a subgame cannot be solved in isolation and must instead consider the strategy for the entire game as a whole, unlike perfect-information games.

Nevertheless, it is possible to first approximate a solution for the whole game and then improve it by solving individual subgames.

This is referred to as subgame solving.

We introduce subgame-solving techniques that outperform prior methods both in theory and practice.

We also show how to adapt them, and past subgame-solving techniques, to respond to opponent actions that are outside the original action abstraction; this significantly outperforms the prior state-of-the-art approach, action translation.

Finally, we show that subgame solving can be repeated as the game progresses down the game tree, leading to far lower exploitability.

These techniques were a key component of Libratus, the first AI to defeat top humans in heads-up no-limit Texas hold'em poker.

Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions.

To address the problem, we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs.

We compare the proposed framework with competing approaches and present high quality image translation results on various challenging unsupervised image translation tasks, including street scene image translation, animal image translation, and face image translation.

We also apply the proposed framework to domain adaptation and achieve state-of-the-art performance on benchmark datasets.

Example machine-learning applications include inverse problems such as personalized PageRank and sampling on graphs.

We provably show that our coded-computation technique can reduce the mean-squared error under a computational deadline constraint.

In fact, the ratio of mean-squared error of replication-based and coded techniques diverges to infinity as the deadline increases.

Further, unlike coded-computation techniques proposed thus far, our strategy combines outputs of all workers, including the stragglers, to produce more accurate estimates at the computational deadline.

The simple closed-form screening rule is a necessary and sufficient condition for exactly recovering the blockwise structure of a solution under any given regularization parameters.

With enough sparsity, the screening rule can be combined with various optimization procedures to deliver solutions efficiently in practice.

The screening rule is especially suitable for large-scale exploratory data analysis, where the number of variables in the dataset 1æéç¡æã®ã«ãžãããã¬ã€ be thousands while we are only interested in the relationship among a handful of variables within moderate-size clusters for interpretability.

Experimental results on various datasets demonstrate the efficiency and insights gained from the introduction of the screening rule.

By exploiting the strong convexity, previous studies have shown that the dynamic regret can be upper bounded by the path-length of the comparator sequence.

In this paper, we illustrate that the dynamic regret can be further improved by allowing the learner to query the gradient of the function multiple times, and meanwhile the strong convexity can be weakened to other non-degenerate conditions.

Specifically, we introduce the squared path-length, which could be much smaller than the path-length, as a new regularity of the comparator sequence.

When multiple gradients are accessible to the learner, we first demonstrate that the dynamic regret of strongly convex functions can be upper bounded by the minimum of the path-length and the squared path-length.

We then extend our theoretical guarantee to functions that are semi-strongly convex or self-concordant.

To the best of our knowledge, this is the first time that semi-strong convexity and self-concordance are utilized to more info the dynamic regret.

State-of-the-art models often use very deep networks with a large number of floating point operations.

Efforts such as model compression learn compact models with fewer number of parameters, but with much reduced accuracy.

Although knowledge distillation has demonstrated excellent improvements for simpler classification setups, the complexity of detection poses new challenges in the form of regression, region proposals and less voluminous la- bels.

We address this through visit web page innovations such as a weighted cross-entropy loss to address class imbalance, a teacher bounded loss to handle the regression component and adaptation layers to better learn from intermediate teacher distribu- tions.

We conduct comprehensive empirical evaluation with different distillation configurations over multiple datasets including PASCAL, KITTI, ILSVRC and MS-COCO.

Our results show consistent improvement in accuracy-speed trade-offs for modern multi-class detection models.

This is done by learning a mapping that maintains the distance between a pair of samples.

Moreover, good mappings are obtained, even by maintaining the distance between different parts of the same sample before and after mapping.

We present experimental results that the new method not only allows for one sided mapping learning, but also leads to preferable numerical results over the existing circularity-based constraint.

We include our prior in a formulation of image restoration as a Bayes estimator that also allows us to solve noise-blind image restoration problems.

We show that the gradient of our prior corresponds to the mean-shift vector on the natural image distribution.

In addition, we learn the mean-shift vector field using denoising autoencoders, and use it in a gradient descent approach to perform Bayes risk minimization.

We demonstrate competitive results for noise-blind deblurring, super-resolution, and demosaicing.

MP and FW address optimization over the linear span and the convex hull of a set of atoms, respectively.

In this paper, we consider the intermediate case of optimization over the convex cone, parametrized as the conic hull of a generic atom set, leading to the first principled definitions of non-negative MP algorithms for which we give explicit convergence rates and demonstrate excellent empirical performance.

Furthermore, we establish a clear correspondence of our algorithms to known algorithms from the MP and FW literature.

Our novel algorithms and analyses target general atom sets and general objective functions, and hence are directly åŒ·çã®ãããã¯ãè§£é€ããã²ãŒã ããã66 to a large variety of learning settings.

Nevertheless, the reason for observations being missing often depends on the unseen observations themselves, and thus the missing data in practice usually occurs in a nonuniform and deterministic fashion rather than randomly.

Equipped with this new tool, we prove a series of theorems for missing data recovery and matrix completion.

In particular, we prove that the exact solutions that identify the target matrix are included as critical points by the commonly used nonconvex programs.

Unlike the existing theories for nonconvex matrix completion, which are built upon the same condition as convex programs, our theory shows that nonconvex programs have the potential to work with a much weaker condition.

Comparing to the existing studies on nonuniform sampling, our setup is more general.

Utilizing the theory of reproducing kernels, we reduce this hypothesis to a simple one-sided score test for a scalar parameter, develop a testing procedure that is robust against the mis-specification of kernel functions, and also propose an ensemble-based estimator for the null model to guarantee test performance in small samples.

To demonstrate the utility of the proposed method, we apply our test to the problem of detecting nonlinear interaction between groups of continuous features.

We evaluate the finite-sample performance of our test under different data-generating functions and estimation strategies for the null model.

It is possible to cause a neural network used for image recognition to misclassify its input by applying very specific, hardly perceptible perturbations to the input, called adversarial perturbations.

Many hypotheses have been proposed to explain the existence of these peculiar samples as well as several methods to mitigate them.

A proven explanation remains elusive, however.

In this work, we take steps towards a formal characterization of adversarial perturbations by deriving lower bounds on the magnitudes of perturbations necessary to change the classification of neural networks.

The bounds are experimentally verified on the MNIST and CIFAR-10 data sets.

Submodular functions can be efficiently minimized and are conse- quently heavily applied in machine learning.

There are many cases, however, in which we do not know the function we aim to optimize, but rather have access to training data that is used to learn the function.

In this paper we consider the question of whether submodular functions can be minimized in such cases.

We show that even learnable submodular functions cannot be minimized within any non-trivial approximation when given access to polynomially-many samples.

We employ a reclassification-by-synthesis algorithm to perform training using a formulation stemmed from the Bayes theory.

Our ICN tries to iteratively: 1 synthesize pseudo-negative samples; and 2 enhance itself by improving the classification.

The single CNN classifier learned is at the same time generative --- being able to directly synthesize new samples within its own discriminative model.

We conduct experiments on benchmark datasets including MNIST, CIFAR-10, and SVHN using state-of-the-art CNN architectures, and observe improved classification results.

Current LDL methods have either restricted assumptions on the expression form of the label distribution or visit web page in representation learning, e.

This paper presents label distribution learning forests LDLFs - a novel label distribution learning algorithm based on differentiable decision trees, which have several advantages: 1 Decision trees have the potential to model any general form of label distributions by a mixture of leaf node predictions.

We define a distribution-based loss function for a forest, enabling all the trees to be learned jointly, and show that an update function for leaf node predictions, which guarantees a strict decrease of the loss function, can be derived by variational bounding.

The effectiveness of the proposed LDLFs is verified on several LDL tasks and a computer vision application, showing significant improvements to the state-of-the-art LDL methods.

Starting from the recent idea of viewpoint factorization, we propose a new approach that, given a large number of images of an object and no other supervision, can extract a dense object-centric coordinate frame.

This coordinate frame is invariant to deformations of the images and comes with a dense equivariant congratulate, å®ç³ãã¡ãã°ããç¡æã²ãŒã ããã¬ã€ãã are neural network that can map image pixels to their corresponding object coordinates.

We demonstrate the applicability of this method to simple articulated objects and deformable objects such as human faces, learning embeddings from random synthetic transformations or optical flow correspondences, all without any manual supervision.

Unfortunately, the huge number of units of these networks makes them expensive both computationally and memory-wise.

To overcome this, exploiting the fact that deep networks are over-parametrized, several compression strategies have been proposed.

These methods, however, typically start from a network that has been trained in a standard manner, without considering such a future compression.

In this paper, we propose to explicitly account for compression in the training process.

To this end, we introduce a regularizer that encourages the parameter matrix of each layer to have low rank during training.

We show that accounting for compression during training allows us to learn much more compact, yet at least as effective, models than state-of-the-art compression techniques.

State-of-the-art decoders deployed in human iBCIs are derived from a Kalman filter that assumes Markov dynamics on the angle of intended movement, and a unimodal dependence on intended angle for each channel of neural activity.

Due to errors made in the decoding of noisy neural data, as a user attempts to move the cursor to a goal, the angle between cursor and goal positions may change rapidly.

This multiscale model explicitly captures the relationship between instantaneous angles of motion and long-term goals, and incorporates semi-Markov dynamics for motion trajectories.

We also introduce a multimodal likelihood model for recordings of neural populations which can be rapidly calibrated for clinical applications.

In offline experiments with recorded neural data, we demonstrate significantly improved prediction of motion directions compared to the Kalman filter.

We derive an efficient online inference algorithm, enabling a clinical trial participant with tetraplegia to control a computer cursor with neural activity in real time.

The observed kinematics of cursor movement are objectively straighter and smoother than prior iBCI decoding models without loss of responsiveness.

This paper models these structures by presenting a predictive recurrent neural network PredRNN.

This architecture is enlightened by åŒ·çã®ãããã¯ãè§£é€ããã²ãŒã ããã66 idea that spatiotemporal predictive learning should memorize both spatial see more and temporal variations in a unified memory pool.

Concretely, memory states are no longer constrained inside each LSTM unit.

Instead, they are allowed to zigzag in two directions: across stacked RNN layers vertically and through all RNN states horizontally.

The core of this network is a new Spatiotemporal LSTM ST-LSTM unit that extracts and memorizes spatial click temporal representations simultaneously.

PredRNN achieves the state-of-the-art prediction performance on three video prediction datasets and is a more general framework, that can be easily extended to other predictive learning tasks by integrating with other architectures.

Recent work has highlighted the power-law multi-time scale properties of brain signals; however, there remains a lack of methods to specifically quantify short- vs.

In this paper, using detrended partial cross-correlation analysis DPCCAwe propose a novel functional connectivity measure to delineate brain interactions at multiple time scales, while controlling for covariates.

We use a rich simulated fMRI dataset to validate the proposed method, and apply it to a real fMRI dataset in a cocaine dependence prediction task.

We show that, compared to extant methods, the DPCCA-based approach not only distinguishes short and long memory functional connectivity but also improves feature extraction and enhances classification accuracy.

Together, this paper contributes broadly to new computational methodologies in understanding neural information processing.

However, the distinctiveness of natural descriptions is often overlooked in previous work.

It is closely related to the quality of captions, as distinctive captions are more likely to describe images with their unique aspects.

In this work, we propose a new learning method, Contrastive Learning CLfor image captioning.

Specifically, via two constraints formulated on top of a reference model, the proposed method can encourage distinctiveness, while maintaining the source quality of the generated captions.

We tested our method on two challenging datasets, where it improves the baseline model by significant margins.

We also showed in our studies that the proposed method is generic and can be used for models with various structures.

However, to find optimal policies, most reinforcement learning algorithms explore all possible actions, which may be harmful for real-world systems.

As a consequence, learning algorithms are rarely applied on safety-critical systems in the real world.

In this paper, we present a learning algorithm that explicitly considers safety, defined in terms of stability guarantees.

Specifically, we extend control-theoretic results on Lyapunov stability verification and show how to use statistical models of the dynamics to obtain high-performance control policies with provable stability certificates.

Moreover, under additional regularity assumptions in terms of a Gaussian process prior, we prove that one can effectively and safely collect data in order to learn about the dynamics and thus both improve control performance and expand the safe region of the state space.

In our experiments, we show how the resulting algorithm can safely optimize a neural network policy on a simulated inverted pendulum, without the pendulum ever falling down.

However, the multiclass extension is in the batch setting and the online extensions only consider binary classification.

We fill this gap in the literature by defining, and justifying, a weak learning condition for online multiclass boosting.

This condition leads to an optimal boosting algorithm that requires the minimal number of weak learners to achieve a certain accuracy.

Additionally, we propose an adaptive algorithm which is near optimal and enjoys an excellent performance on real data due to its adaptive property.

Matching is an effective strategy to tackle this problem.

The widely used matching estimators such as nearest neighbor matching NNM pair the treated units with the most similar control units in terms of covariates, and then estimate treatment effects accordingly.

However, the existing matching estimators have poor performance when the distributions of control and treatment groups are unbalanced.

Moreover, theoretical analysis suggests that the bias of causal effect estimation would increase with the dimension of covariates.

In this paper, we aim to address these problems by learning low-dimensional balanced and nonlinear representations BNR for observational data.

In particular, we convert counterfactual prediction as a classification problem, develop a kernel learning model with domain adaptation constraint, and design a novel matching estimator.

The dimension of covariates will be significantly reduced after projecting data to a low-dimensional subspace.

Experiments on several synthetic and real-world datasets demonstrate the effectiveness of our approach.

Despite having significant practical importance, such HMMs are poorly understood with no known positive or negative results for efficient learning.

In this paper, we present several new results---both positive and negative---which åŒ·çã®ãããã¯ãè§£é€ããã²ãŒã ããã66 define check this out boundaries between the tractable-learning setting and the intractable setting.

We show positive results for a large subclass of HMMs whose transition matrices this æŒ«ç»æŒ«ç»ã²ãŒã æ¶é²å£«ãµã question sparse, well-conditioned and have small probability mass on short cycles.

We also show that learning is impossible given only a polynomial number of samples for HMMs with a small output alphabet and whose transition matrices are random regular graphs with large degree.

We also discuss these results in the context of learning HMMs which can capture long-term dependencies.

Here we propose to model article source causal interaction using integro-differential equations and causal kernels that allow for a rich analysis of effective connectivity.

The approach combines the tractability and flexibility of autoregressive modeling with the biophysical interpretability of dynamic causal modeling.

The causal kernels are learned nonparametrically using Gaussian process regression, yielding an efficient framework for causal inference.

We construct a novel class of causal covariance functions that enforce the desired properties of the causal kernels, an approach which we call GP CaKe.

By construction, the model and its hyperparameters have biophysical meaning and are therefore easily interpretable.

We demonstrate the efficacy of GP CaKe on a number of simulations and give an example of a realistic application on magnetoencephalography MEG data.

A useful approach to obtain data is to be creative and mine data from various sources, that were created for different purposes.

Unfortunately, this approach often leads to noisy labels.

In this paper, we propose a meta algorithm for tackling the noisy labels problem.

We demonstrate the effectiveness of our algorithm by mining data for gender classification by combining the Labeled Faces in the Wild LFW face recognition dataset with a textual genderizing service, which leads to a noisy dataset.

While our approach is very simple to implement, it leads to state-of-the-art results.

We analyze some convergence properties of the proposed algorithm.

However, success stories of Deep Learning with standard feed-forward neural networks FNNs are rare.

FNNs that perform well are typically shallow and, therefore cannot exploit many levels of abstract representations.

We introduce self-normalizing neural networks SNNs to enable high-level abstract representations.

While batch normalization requires explicit normalization, neuron activations of SNNs automatically converge towards åŒ·çã®ãããã¯ãè§£é€ããã²ãŒã ããã66 mean and unit variance.

The activation function of SNNs are ""scaled exponential linear units"" SELUswhich induce self-normalizing properties.

Using the Banach fixed-point theorem, we prove that activations close to zero mean and unit variance that ãã¢ã«ãžãã©ã»ã€ã³ãã§ã€ã¹ããã¯ propagated through many network layers will converge towards zero mean and unit variance -- even under the presence of noise and perturbations.

This convergence property of SNNs allows to 1 train deep networks with many layers, 2 employ strong regularization, and 3 to make learning highly robust.

Furthermore, for activations not close to unit variance, we prove an upper and lower bound on the variance, thus, vanishing and exploding gradients are impossible.

We compared SNNs on a 121 tasks from the UCI machine learning repository, on b drug discovery benchmarks, and on c astronomy tasks with standard FNNs and other machine learning methods such as random forests and support vector machines.

For FNNs we considered i ReLU networks without normalization, ii batch normalization, iii layer normalization, iv weight normalization, v highway networks, vi residual networks.

SNNs significantly outperformed all competing FNN methods at 121 UCI tasks, outperformed all competing methods at the Tox21 dataset, and set a new record at an astronomy data set.

The winning SNN architectures are often very deep.

The majority of this work focuses on a binary domain label.

Similar problems occur in a scientific context where there may be a continuous family of plausible data generation processes associated to the presence of systematic uncertainties.

Robust inference is possible if it is based on a pivot -- a quantity whose distribution does not depend on the unknown values of the nuisance parameters that parametrize this https://list-games-promocode.site/1/1022.html of data generation processes.

In this work, we introduce and derive theoretical results for a training procedure based on adversarial networks for enforcing the pivotal property or, equivalently, fairness with respect to continuous attributes on a predictive model.

The method includes a hyperparameter to control the trade-off between accuracy and robustness.

We demonstrate the effectiveness of this approach with a toy example and examples from particle physics.

While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not restricted to a grid.

Instead, their precise locations contain essential physical information, that would get lost if discretized.

Thus, we propose to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid.

We apply those layers in SchNet: a novel deep learning architecture modeling quantum interactions in molecules.

We obtain a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles.

Our architecture achieves state-of-the-art performance for benchmarks of equilibrium molecules and molecular dynamics trajectories.

Finally, we introduce a more challenging benchmark with chemical and structural variations that suggests the path for further work.

This paper presents two improved alternatives based on lightweight estimates of sample uncertainty in stochastic gradient descent SGD : the variance in predicted probability of the correct class across iterations of mini-batch SGD, and the proximity of the correct class probability to the decision threshold.

Extensive experimental results on six datasets show that our methods reliably improve accuracy in various network architectures, including additional gains on top of other popular training techniques, such as residual learning, momentum, ADAM, batch normalization, dropout, and distillation.

For example, is it possible to use in deep architectures a layer whose output is the minimal cut of a parametrized graph?

Given that these models are trained end-to-end by leveraging gradient information, the introduction of such layers seems very challenging due to their non-continuous output.

In this paper we focus on the problem of submodular minimization, for which we show that such layers are indeed possible.

The key idea is that we can continuously relax the output without sacrificing guarantees.

We provide an easily computable approximation to the Jacobian complemented with a complete theoretical analysis.

Finally, these contributions let us experimentally learn probabilistic log-supermodular models via a bi-level variational inference formulation.

However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes.

Here we present GraphSAGE, a general, inductive framework that leverages node feature information e.

Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood.

Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we ãŠãã³ãŒã³ããã·ã¥ã²ãŒã ããªã³ã©ã€ã³ã§ãã¬ã€ the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.

Sequential data, including time-series and ordered data, contain important structural relationships among items, imposed by underlying dynamic models of data, that should play a vital role in the selection of representatives.

However, nearly all existing subset selection techniques ignore underlying dynamics of data and treat items independently, leading to incompatible sets of representatives.

In this paper, we develop a new framework for sequential subset selection that finds a set of ã¹ããŒã¯ãšã¯ããã®ã¹ããã compatible with the dynamic models of data.

To do so, we equip items with transition dynamic models and pose the problem as an integer binary optimization over assignments of sequential items to representatives, that leads to high encoding, diversity and transition potentials.

Our formulation generalizes the well-known facility location objective to deal with sequential data, incorporating transition dynamics among facilities.

As the proposed formulation is non-convex, we derive a max-sum message passing algorithm to solve the problem efficiently.

Experiments on synthetic and real data, including instructional video summarization, show that our sequential subset selection framework not only achieves better encoding and diversity than the state of the art, but also successfully incorporates dynamics of data, leading to compatible representatives.

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The capacity of an LSTM network can be increased by widening and adding layers.

However, usually the former introduces additional parameters, while the latter increases the runtime.

As an alternative we propose the Tensorized LSTM in which the hidden states are represented by tensors and updated via a cross-layer convolution.

By increasing the tensor size, the network can be widened efficiently without additional parameters since the parameters are shared across different locations in the tensor; by delaying the output, the network can be deepened implicitly with little additional runtime since deep computations for each timestep are merged into temporal computations of the sequence.

Experiments conducted on five challenging sequence learning tasks show the potential of the proposed model.

The capacity of an LSTM network can be increased by widening and adding layers.

However, usually the former introduces additional parameters, while the latter increases the runtime.

As an alternative we propose the Tensorized LSTM in which the hidden states are represented by tensors and updated via a cross-layer convolution.

By increasing the tensor size, the network can be widened efficiently without additional parameters since the parameters are shared across different locations in the tensor; by delaying the output, the network can be deepened implicitly with little additional runtime since deep computations for each timestep are merged into temporal computations of the sequence.

Experiments conducted on five challenging sequence learning tasks show the potential of the proposed model.

~the number of nodes in the Ising model.

We show that our results are optimal up to logarithmic factors in the dimension.

We obtain our results by extending and strengthening the exchangeable-pairs approach used to prove concentration of measure in this setting by Chatterjee.

We demonstrate the efficacy of such functions as statistics for testing the strength of interactions in social networks in both synthetic and real world data.

~the number of nodes in the Ising model.

We show that our results are optimal up to logarithmic factors in the dimension.

We obtain our results by extending and strengthening the exchangeable-pairs approach used to prove concentration of measure in this setting by Chatterjee.

We demonstrate the efficacy of such functions as statistics for testing the strength of interactions in social networks in both synthetic and real world data.

This architecture is built upon deep auto-encoders, which non-linearly map the input data into a latent space.

Being differentiable, our new self-expressive layer provides a simple but effective way to learn pairwise affinities between all data points through a standard back-propagation procedure.

Being nonlinear, our neural-network based method is able to cluster data points having complex often nonlinear structures.

We further propose pre-training and fine-tuning strategies that let us effectively learn the parameters of our subspace clustering networks.

Our experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised subspace clustering methods.

This architecture is built upon deep auto-encoders, which non-linearly map the input ã¹ã¯ã©ããã«ãŒããªã into a latent space.

Being differentiable, our new self-expressive layer provides a simple but effective way to learn pairwise affinities between all data points through a standard back-propagation procedure.

Being nonlinear, our neural-network based method is able to cluster data points having complex often nonlinear structures.

We further propose pre-training and fine-tuning strategies that let us effectively learn the parameters of our subspace clustering networks.

Our experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised subspace clustering methods.

Our proposed attention module can be trained with or without extra supervision, and gives a sizable boost in accuracy while keeping the network size and computational cost nearly the same.

It leads to significant improvements over state of the art base architecture on three standard action recognition benchmarks across still images and videos, and establishes new state of the art on MPII 12.

We also perform an extensive analysis of our attention module both empirically and analytically.

In terms of the latter, we introduce a novel derivation of bottom-up and top-down attention as low-rank approximations of bilinear pooling methods typically used ãµã€ãã³ã«ãžã fine-grained classification.

From this perspective, our attention formulation suggests a novel characterization of action recognition as a fine-grained recognition problem.

Our proposed attention module can be trained with or without extra supervision, and gives a sizable boost in accuracy while keeping the network size and computational cost nearly the same.

It leads to significant improvements over state of the art base architecture on three standard action recognition benchmarks across still images and videos, and establishes new state of the art on MPII 12.

We also perform an extensive analysis of our attention module both empirically and analytically.

In terms of the latter, we introduce a novel derivation of bottom-up and top-down attention as low-rank approximations of bilinear pooling methods typically used for fine-grained classification.

From this perspective, our attention formulation suggests a novel characterization of action recognition as a fine-grained recognition problem.

We present finite sample statistical consistency guarantees for Quick Shift on mode and cluster recovery under mild distributional assumptions.

We then apply our results to construct a consistent modal regression algorithm.

We present finite sample statistical consistency guarantees for Quick Shift on mode and cluster recovery under mild distributional assumptions.

We then apply our results to construct a consistent modal regression algorithm.

Yet, despite their practical success, support for nonsmooth objectives is still lacking, making them unsuitable for many problems of interest in machine learning, such as the Lasso, group Lasso or empirical risk minimization with convex constraints.

In this work, we propose and analyze ProxASAGA, a fully asynchronous sparse method inspired by SAGA, a variance reduced incremental gradient algorithm.

The proposed method is easy to implement and significantly outperforms the state of the art on several nonsmooth, large-scale problems.

We prove that our method achieves a theoretical linear speedup with respect to the sequential version under assumptions on the sparsity of gradients and block-separability of the proximal term.

Empirical benchmarks on a multi-core architecture illustrate practical speedups of up to 12x on a 20-core machine.

Yet, despite their practical success, support for nonsmooth objectives is still lacking, making them unsuitable for many problems of interest in machine learning, such as the Lasso, group Lasso or empirical risk minimization with convex constraints.

In this work, we propose and analyze ProxASAGA, a fully asynchronous sparse method inspired by SAGA, a variance reduced incremental gradient algorithm.

The proposed method is easy to implement and significantly outperforms the state of the art on several nonsmooth, large-scale problems.

We prove that our ç¡æã«ãžãã®ã¹ããããã·ã³ achieves a theoretical linear speedup with respect to the sequential version under assumptions on the sparsity of gradients and block-separability of the proximal term.

Empirical benchmarks on a multi-core architecture illustrate practical speedups of up to 12x on a 20-core machine.

However, learning from synthetic faces may not achieve the desired performance due to the discrepancy between distributions of the synthetic and real face images.

The dual agents are specifically designed for distinguishing real v.

In particular, we employ an off-the-shelf 3D face model as a simulator to generate profile face images with varying poses.

DA-GAN leverages a fully convolutional network as the generator to generate high-resolution images and an auto-encoder as the discriminator with the dual agents.

Besides the novel architecture, we make several key modifications to the standard GAN to preserve pose and texture, preserve identity and stabilize training process: i a pose perception loss; ii an identity perception loss; iii an adversarial ãªã³ã©ã€ã³ã§ãã¬ã€ããèµ€ã¡ããçããŠããã²ãŒã with a boundary equilibrium regularization term.

Experimental results show that DA-GAN not only presents compelling perceptual results but also significantly outperforms state-of-the-arts on the large-scale and challenging NIST IJB-A unconstrained face recognition benchmark.

In addition, the proposed DA-GAN is also promising as a new approach for solving generic transfer learning problems more effectively.

However, learning from synthetic faces may not achieve the desired performance due to the discrepancy between distributions of the synthetic and real face images.

The dual agents are specifically designed for distinguishing real v.

In particular, we employ an off-the-shelf 3D face model as a simulator to generate profile face images with varying poses.

DA-GAN leverages a fully convolutional network as the generator to generate high-resolution images and an auto-encoder as the discriminator with the dual agents.

Besides the novel architecture, we make several key modifications to the standard GAN to preserve pose and texture, preserve identity and stabilize training process: i a pose perception loss; ii an identity perception loss; iii an adversarial loss with a boundary equilibrium regularization term.

Experimental results show that DA-GAN not only presents compelling perceptual results but also significantly outperforms state-of-the-arts on the large-scale and challenging NIST IJB-A unconstrained face recognition benchmark.

In addition, the proposed DA-GAN is also promising as a new approach for solving generic transfer learning problems more effectively.

There are three major challenges: 1 complex dependencies, 2 vanishing and exploding gradients, and 3 efficient parallelization.

In this paper, we introduce a simple yet effective RNN connection structure, the Here, which simultaneously tackles all of these challenges.

The proposed architecture is characterized by multi-resolution dilated recurrent skip connections and can be combined flexibly with diverse RNN cells.

Moreover, the DilatedRNN reduces the number of parameters needed and enhances training efficiency significantly, while matching state-of-the-art performance even with standard RNN cells in tasks involving very long-term dependencies.

We rigorously prove the advantages of the DilatedRNN over other recurrent neural architectures.

There are three major challenges: 1 complex dependencies, 2 vanishing and exploding gradients, and 3 efficient parallelization.

In this paper, we introduce a simple yet effective RNN connection structure, the DilatedRNN, which simultaneously tackles all of these challenges.

The proposed architecture is characterized by multi-resolution dilated recurrent skip connections and can be combined flexibly with diverse RNN cells.

Moreover, the DilatedRNN reduces the number of parameters needed and enhances training efficiency significantly, while matching state-of-the-art performance even with standard RNN cells in tasks involving very long-term dependencies.

We rigorously prove the advantages of the DilatedRNN over other recurrent neural architectures.

This leads to the discovery of family of graph spectral distances denoted as FGSD and their based graph feature representations, which we prove to possess most of these desired properties.

To both evaluate the quality of graph features produced by FGSD and demonstrate their utility, we apply them to the graph classification problem.

Through extensive experiments, we show that a simple SVM based classification algorithm, driven with our powerful FGSD based graph features, significantly outperforms all the more sophisticated state-of-art algorithms on the unlabeled node datasets in terms of both accuracy and speed; it also yields very competitive results on the labeled datasets - despite the fact it does not utilize any node label information.

This leads to the discovery of family of graph spectral distances denoted as FGSD and their based graph feature representations, which we prove to possess most of these desired properties.

To both evaluate the quality of graph features produced by FGSD and demonstrate their utility, we apply them to the graph classification problem.

Through extensive experiments, we show that a simple SVM based classification algorithm, driven with our powerful FGSD based graph features, significantly outperforms all the more sophisticated state-of-art algorithms on the unlabeled node datasets in terms of both accuracy and speed; it also yields very competitive results on the labeled datasets - despite the fact it does not utilize any node label information.

However, existing GLBs scale poorly with the number of rounds and the number of arms, limiting their utility in practice.

This paper proposes new, scalable solutions to the GLB problem in two respects.

As a special case, we apply GLOC to the online Newton step algorithm, which results in a low-regret GLB algorithm with much lower time and memory complexity than prior work.

Such methods can be implemented via hashing algorithms i.

Finally, we propose a fast approximate hash-key computation inner product with a better accuracy than the state-of-the-art, which can be of independent interest.

We conclude the paper with preliminary experimental results confirming the merits of our methods.

However, existing GLBs scale poorly with the number of rounds and the number of arms, limiting their utility in practice.

This paper proposes new, scalable solutions to the GLB problem in two respects.

As a special case, we apply GLOC to the online Newton step algorithm, which results in a low-regret GLB algorithm with much lower time and memory complexity than prior work.

Such methods can be implemented via hashing algorithms i.

Finally, we propose a fast approximate hash-key computation inner product with a better accuracy than the state-of-the-art, which can be of independent interest.

We conclude the paper with preliminary experimental results confirming the merits of our methods.

The result is a posterior distribution over the integral that explicitly accounts for dual sources of numerical approximation error due to a severely limited computational budget.

This construction is applied to account, in a statistically principled manner, for the impact of numerical errors that at present are confounding factors in functional cardiac model assessment.

The result is a posterior distribution over the integral that explicitly accounts for dual sources of numerical approximation error due to a severely limited computational budget.

This construction is applied to account, in a statistically principled manner, for the impact of numerical errors that at present are confounding factors in functional cardiac model assessment.

So far, distributed machine learning frameworks have largely ignored the possibility of failures, especially arbitrary i.

Causes of failures include software bugs, network asynchrony, biases in local datasets, as well as attackers trying to compromise the entire system.

We first show that no gradient aggregation rule based on a linear combination of the vectors proposed by the workers i.

We also report on experimental evaluations of Krum.

So far, distributed machine learning frameworks have largely ignored the possibility of failures, especially arbitrary i.

Causes of failures include software bugs, network asynchrony, biases in local datasets, as well as attackers trying to compromise the entire system.

We first show that no gradient aggregation rule based on a linear combination of the vectors proposed by the workers i.

We also report on experimental evaluations of Krum.

Sometimes, it is desirable for a human operator to interrupt an agent in order to prevent dangerous situations from happening.

Yet, as part of their learning process, agents may link these interruptions, that impact their reward, to specific states and deliberately avoid them.

The situation is particularly challenging in a multi-agent context because agents might not only learn from their own past interruptions, but also from those of other agents.

Orseau and Armstrong defined safe interruptibility for one learner, but their work does not naturally extend to multi-agent systems.

This paper introduces dynamic safe interruptibility, an alternative definition more suited to decentralized learning problems, and studies this notion in two learning frameworks: joint action learners and independent learners.

We give realistic sufficient conditions on the learning algorithm to enable dynamic safe interruptibility in the case of joint action learners, yet show that these conditions are not sufficient for independent learners.

We show however that if agents can detect interruptions, it is possible to prune the observations to ensure dynamic safe interruptibility even for independent learners.

Sometimes, it is desirable for a human operator to interrupt an agent in order to prevent dangerous situations from happening.

Yet, as part of their learning process, agents may link these interruptions, that impact their reward, to specific states and deliberately avoid them.

The situation is particularly challenging in a multi-agent context because agents might not only learn from their own past interruptions, but also from those of other agents.

Orseau and Armstrong defined safe interruptibility for one learner, but their work does not naturally extend to multi-agent systems.

This paper introduces dynamic safe interruptibility, an alternative definition more suited to decentralized learning problems, and studies this notion in two learning frameworks: joint action learners and independent learners.

We give realistic sufficient conditions on the learning algorithm to enable dynamic safe interruptibility in the case of joint action learners, yet show that these conditions are not sufficient for independent learners.

We show however that if agents can detect interruptions, it is possible to prune the observations to ensure dynamic safe interruptibility even for independent learners.

In real life situations, however, the utility function is not fully known in advance and can only be estimated via interactions.

For instance, whether a user likes a movie or not can be reliably evaluated only after it was shown to her.

Or, the range of influence of a user in a social network can be estimated only after she is selected to advertise the product.

We model such problems as an interactive submodular bandit optimization, where in each round we receive a context e.

We then receive a noisy feedback about the utility of the action e.

Given a bounded-RKHS norm kernel over the context-action-payoff space that governs the smoothness of the utility function, SM-UCB keeps an upper-confidence bound on the payoff function that allows it to asymptotically achieve no-regret.

Finally, we evaluate our results on four concrete applications, including movie recommendation on the MovieLense data setnews recommendation on Yahoo!

Webscope datasetinteractive influence maximization on a subset of the Facebook networkand personalized data summarization on Reuters Corpus.

In all these applications, we observe that SM-UCB consistently outperforms the prior art.

In real life situations, however, the utility function is not fully known in advance and can only be estimated via interactions.

For instance, whether a user likes a movie or not can be reliably evaluated only after it was shown to her.

Or, the range of influence of a user in a social network can be estimated only after she is selected to article source the product.

We model such problems as an interactive submodular bandit optimization, where in each round we receive a context e.

We then receive a noisy feedback about the utility of the action e.

Given a bounded-RKHS norm kernel over the context-action-payoff space that governs the smoothness of the utility function, SM-UCB keeps an upper-confidence bound on the payoff function that allows it to asymptotically achieve no-regret.

Finally, we evaluate our results on four concrete applications, including movie recommendation on the MovieLense data setnews recommendation on Yahoo!

Webscope datasetinteractive influence maximization on a subset of the Facebook networkand personalized data summarization on Reuters Corpus.

In all these applications, we observe that SM-UCB consistently outperforms the prior art.

At the core of our system is a physical world representation that is first recovered by a perception module and then utilized by check this out and graphics engines.

During training, the perception module and the generative models learn by visual de-animation --- interpreting and reconstructing the visual information stream.

During testing, the system first recovers the physical world state, and then uses the generative models for reasoning and future prediction.

Even more so than forward simulation, inverting a physics or graphics engine is a computationally hard problem; we overcome this challenge by using a convolutional inversion network.

Our system quickly recognizes the physical world state from appearance and motion cues, and has the flexibility to incorporate both differentiable and non-differentiable physics and graphics engines.

We evaluate our system on both synthetic and real datasets involving multiple physical scenes, and demonstrate that our system performs well on both physical state estimation and reasoning problems.

We further show that the knowledge learned on the synthetic dataset generalizes to constrained real images.

At the core of our system is a physical world representation that is first recovered by a perception module and then utilized by physics and graphics engines.

During training, the perception module and the generative models learn by visual de-animation --- interpreting and reconstructing the visual information stream.

During testing, the system first recovers the physical world state, and then uses the generative models for reasoning and future prediction.

Even more so than forward simulation, inverting a physics or graphics engine is a computationally hard problem; we overcome this challenge by using a convolutional inversion network.

Our system quickly recognizes the physical world state from appearance and motion cues, and has the flexibility to incorporate both differentiable and non-differentiable physics and graphics engines.

We evaluate our system on both synthetic and real datasets involving multiple physical scenes, and demonstrate that our system performs well on both physical state estimation and reasoning problems.

We further show that the knowledge learned on the synthetic dataset generalizes to constrained real images.

Our approach battles domain shift with a domain adversarial loss, and generalizes the embedding to novel task using a metric learning-based approach.

Our model is simultaneously optimized on labeled source data and unlabeled or sparsely labeled data in the target domain.

Our method shows compelling results on novel classes within a new domain even when only a few labeled examples per class are available, outperforming the prevalent fine-tuning approach.

In addition, we demonstrate the effectiveness of our framework on the transfer learning task from image object recognition to video action recognition.

Our approach battles domain shift with a domain adversarial loss, and generalizes the embedding to novel task using a metric learning-based approach.

Our model is simultaneously optimized on labeled source data and unlabeled or sparsely labeled me? 60å¹Žä»£ã®ã²ãŒã similar in the target domain.

Our method shows compelling results on novel classes within a new domain even when only a few labeled examples per class are available, outperforming the prevalent fine-tuning approach.

In addition, we demonstrate the effectiveness of our framework on the transfer learning task from image object recognition to video action recognition.

However, since it only searches for local optima at each time step through one-step forward looking, it usually cannot output the best target sentence.

Specifically, we propose a recurrent structure for the value network, and train its parameters from bilingual data.

Experiments show that such an approach can significantly improve the translation accuracy on several translation tasks.

However, since it only searches for local optima at each time step through one-step forward looking, it usually cannot output the best target sentence.

Specifically, we propose a recurrent structure for the value network, and train its parameters from bilingual data.

Experiments show that such an approach can significantly improve the translation accuracy on several translation tasks.

PSM offers significant advantages over other competing methods: 1 PSM naturally obtains the complete solution path for all values of the regularization parameter; 2 PSM provides a high precision dual certificate stopping criterion; 3 PSM yields sparse solutions through very few iterations, and the solution sparsity significantly reduces the computational cost per iteration.

Particularly, we demonstrate the superiority of PSM over various sparse learning approaches, including Dantzig selector for sparse linear regression, sparse support vector machine ããã³ã²ãŒã sparse linear classification, and sparse differential network estimation.

We then provide sufficient conditions under which PSM always outputs sparse solutions such that its computational performance can be significantly boosted.

Thorough numerical experiments are provided to demonstrate the outstanding performance of the PSM method.

PSM offers significant advantages over other competing methods: 1 PSM naturally obtains the complete solution path for all values of the regularization parameter; 2 PSM provides a high precision dual certificate stopping criterion; 3 PSM yields sparse solutions through very few iterations, and the solution sparsity significantly reduces the computational cost per iteration.

Particularly, we demonstrate the superiority of PSM over various sparse learning approaches, including Dantzig selector for sparse linear regression, sparse support vector machine for sparse linear classification, and sparse differential network estimation.

We then provide sufficient conditions under which PSM always outputs sparse solutions such that its computational performance can be significantly boosted.

Thorough numerical experiments are provided to demonstrate the outstanding performance of the PSM method.

Among them, learning models with grouped variables have shown competitive performance for prediction and variable selection.

However, the previous works mainly focus on the least squares regression problem, not the classification task.

Thus, it is desired to design the new additive classification model with variable selection capability for many real-world applications which focus on high-dimensional article source classification.

To address this challenging problem, in this paper, we investigate the classification with group sparse additive models in reproducing kernel Hilbert spaces.

Generalization error bound is derived and proved by integrating the sample error analysis with empirical covering numbers and the hypothesis error estimate with the stepping stone technique.

Our new bound shows that GroupSAM can achieve a satisfactory learning rate with polynomial decay.

Experimental results on synthetic data and seven benchmark datasets consistently show the effectiveness of our new approach.

Among them, learning models with grouped variables have shown competitive performance for prediction and variable selection.

However, the previous works mainly focus on the least squares regression problem, not the classification task.

Thus, it is desired to design the new additive classification model with variable selection capability for many real-world applications which focus on high-dimensional data classification.

To address this challenging problem, in this paper, we investigate the classification with group sparse additive models in reproducing kernel Hilbert spaces.

Generalization error bound is derived and proved by integrating the sample error analysis with empirical covering numbers and the hypothesis error estimate with the stepping stone technique.

Our new bound shows that GroupSAM can achieve a satisfactory learning rate with polynomial decay.

Experimental results on synthetic data and seven benchmark datasets consistently show the effectiveness of our new approach.

This is very helpful since inference, or relevant bounds, may be much easier to obtain or more accurate for some model in the class.

Here we introduce methods to extend the approach to models with higher-order potentials and develop theoretical insights.

We demonstrate empirically that rerooting can significantly improve accuracy of methods of inference for higher-order models at negligible computational cost.

This is very helpful since inference, or relevant bounds, may be much easier to obtain or more accurate for some model in the class.

Here we introduce methods to extend the approach to models with higher-order potentials and develop theoretical insights.

We demonstrate empirically that rerooting can significantly improve accuracy of methods of inference for higher-order models at negligible computational cost.

We introduce matrices with complex entries which give significant further accuracy improvement.

We provide geometric and Markov chain-based perspectives to help understand the benefits, and empirical results which suggest that the approach is helpful in a wider range of applications.

We introduce matrices with complex entries which give significant further accuracy improvement.

We provide geometric and Markov chain-based perspectives to help understand the benefits, and empirical results which suggest that the approach is helpful in a wider range of applications.

In this context, a number of recent studies have focused on defining, detecting, and removing unfairness from data-driven decision systems.

However, the existing notions of fairness, based on parity equality in treatment or outcomes for different social groups, tend to be quite stringent, limiting the overall decision making accuracy.

In this paper, we draw inspiration from the fair-division and envy-freeness literature in economics and game theory and propose preference-based notions of fairness -- given the choice between various sets of decision treatments or outcomes, any group of users would collectively prefer its treatment or outcomes, regardless of the dis parity as compared to the other groups.

Then, we introduce tractable proxies to design margin-based classifiers that satisfy these preference-based notions of fairness.

Finally, we experiment with a variety of synthetic and real-world datasets and show that preference-based fairness allows for greater decision accuracy than parity-based fairness.

In this context, a number of recent studies have focused on defining, detecting, and removing unfairness from data-driven decision systems.

However, the existing notions of fairness, based on parity equality in treatment or outcomes for different social groups, tend to be quite stringent, limiting the overall decision making accuracy.

In this paper, we draw inspiration from the fair-division and envy-freeness literature in economics and game theory and propose preference-based notions of fairness -- given the choice between various sets of decision treatments or outcomes, any group of users would collectively prefer its treatment or outcomes, regardless of the dis parity as compared to the other groups.

Then, we introduce tractable proxies to design margin-based classifiers that satisfy these preference-based notions of fairness.

Finally, we experiment with a variety of synthetic and real-world datasets and show that preference-based fairness allows for greater decision accuracy than parity-based fairness.

A popular solution is combining multiple sources of weak supervision using generative models.

The structure of these models affects the quality of the training labels, but is difficult to learn without any ground truth labels.

We instead rely on weak supervision sources having some structure by virtue of being encoded programmatically.

We present Coral, a paradigm that infers generative model structure by statically analyzing the code for these heuristics, thus significantly reducing the amount of data required to learn structure.

Empirically, Coral matches or outperforms traditional structure learning approaches by up to 3.

Using Coral to model dependencies instead of assuming independence results in better performance than a fully supervised model by 3.

A popular solution is combining multiple sources of weak supervision using generative models.

The structure of these models affects the quality of the training labels, but is difficult to learn without any ground truth labels.

We instead rely on weak supervision sources having some structure by virtue of being encoded programmatically.

We present Coral, a paradigm that infers generative model structure by statically analyzing the code for these heuristics, thus significantly reducing the amount of data required to learn structure.

Empirically, Coral matches or outperforms traditional structure learning approaches by up to 3.

Using Coral to model dependencies instead of assuming independence results in better performance than a fully supervised model by 3.

Here we develop structured exponential family embeddings S-EFEa method for discovering embeddings that vary across related groups of data.

We study how the word usage of U.

Congressional speeches varies across states and party affiliation, how words are used differently across sections of the ArXiv, and how the co-purchase patterns of groceries can vary across seasons.

Key to the success of our method is that the ç¡æããŠã³ããŒãPCã²ãŒã bookworm deluxe share statistical information.

We develop two sharing strategies: hierarchical modeling and amortization.

We demonstrate the benefits of this approach in empirical studies of speeches, abstracts, and shopping baskets.

We show how SEFE enables group-specific interpretation of word usage, and outperforms EFE in predicting held-out data.

Here we develop structured exponential family embeddings S-EFEa method for discovering embeddings that vary read article related groups of data.

We study how the word usage of U.

Congressional speeches varies across states and party affiliation, how words are used differently across sections of the ArXiv, and how the co-purchase patterns of groceries can vary across seasons.

Key to the success of our method is that the groups share statistical information.

We develop two sharing strategies: hierarchical modeling and amortization.

We demonstrate the benefits of this approach in empirical studies of speeches, abstracts, and shopping baskets.

We show how SEFE enables group-specific interpretation of word usage, and outperforms EFE in predicting held-out data.

We learn ããŒã¿ã«ãããªã²ãŒã ps3 test features that best indicate the differences between observed samples and a reference model, by minimizing the false negative rate.

We analyse the asymptotic Bahadur efficiency of the new test, and prove that long ã¬ã«ãã¹ããªãŒã ã«ãžãå€§æŠæ¥ sorry a mean-shift alternative, our test always has greater relative efficiency than a previous linear-time kernel test, regardless of the choice of parameters for that test.

In experiments, the performance of our method exceeds that of the earlier linear-time test, and matches or exceeds the power of a quadratic-time kernel test.

In high dimensions and where model structure may be exploited, our goodness of fit test performs far better than a quadratic-time two-sample test based on the Maximum Mean Discrepancy, with samples drawn from the model.

We learn the test features that best indicate the differences between observed samples and a reference model, by minimizing the false negative rate.

We analyse the asymptotic Bahadur efficiency of the new test, and prove that under a mean-shift alternative, our test always has greater relative efficiency than a previous linear-time kernel test, regardless of ã¹ããŒããã©ã³çšã®æé«ã®ã²ãŒã choice of parameters for that test.

In experiments, the performance of our method exceeds that of the earlier linear-time test, and matches or exceeds the power of a quadratic-time kernel test.

In high dimensions and where model structure may be exploited, our goodness of fit test performs far better than a quadratic-time two-sample test based on the Maximum Mean Discrepancy, with samples drawn from the model.

Such stereotyped structure suggests the existence of common computational principles.

However, such principles have remained largely elusive.

Inspired by gated-memory networks, namely long short-term memory networks LSTMswe introduce a recurrent neural network in which information is gated through inhibitory cells that are subtractive subLSTM.

We propose a natural mapping of subLSTMs onto known canonical excitatory-inhibitory cortical microcircuits.

Our empirical evaluation across sequential image classification and language modelling tasks shows that subLSTM units can achieve similar performance to LSTM units.

These results suggest that cortical circuits can be optimised to solve complex contextual problems and proposes a novel view on their computational function.

Overall our work provides a step towards unifying recurrent networks as used in machine learning with their biological counterparts.

Such stereotyped structure suggests the existence of common computational principles.

However, such principles have remained largely elusive.

Inspired by gated-memory networks, namely long short-term memory networks LSTMswe introduce a recurrent neural network in which information is gated through inhibitory cells that are subtractive subLSTM.

We propose a natural mapping of subLSTMs onto known canonical excitatory-inhibitory cortical microcircuits.

Our empirical evaluation across sequential image classification and language modelling tasks shows that subLSTM units can achieve similar performance to LSTM units.

These results suggest that cortical circuits can be optimised to solve complex contextual problems and proposes a novel view on their computational function.

Overall our work provides a step towards unifying recurrent networks as used in machine learning with their biological counterparts.

We study the norms obtained from extending the k-support norm and OWL norms to the setting in which there are overlapping groups.

The resulting norms are in general NP-hard to compute, but they are tractable for certain collections of groups.

To demonstrate this fact, we develop a dynamic program for the problem of projecting onto the set of vectors supported by a fixed number of groups.

Our dynamic program utilizes tree decompositions and its complexity scales with the treewidth.

This program can be converted to an extended formulation which, for the associated group structure, models the k-group support norms and an overlapping group variant of the ordered weighted l1 norm.

Numerical results demonstrate the efficacy of the new penalties.

We study the norms obtained from extending the k-support norm and OWL norms to the setting in which there are overlapping groups.

The resulting norms are in general NP-hard to compute, but they are tractable for certain collections of groups.

To demonstrate this fact, we develop a dynamic program for the problem of projecting onto the set of vectors supported by a fixed number of groups.

Our dynamic program utilizes tree decompositions and its complexity scales with the treewidth.

This program can be converted to an extended formulation which, for the associated group structure, models the k-group support norms and an overlapping group variant of the ordered weighted l1 norm.

Numerical results demonstrate the efficacy of the new penalties.

We show that while it is sensible to think of recall as simply retrieving items when probed with a cue - typically the item list itself - it is better to think of recognition as retrieving cues when probed with items.

To test this theory, by manipulating the number of items and cues in a memory experiment, we show a crossover effect in memory performance within subjects such that recognition performance is superior to recall performance when the number of items is greater than the number of cues and recall performance is better than recognition when the converse holds.

We build a simple computational model around this theory, using sampling to approximate an ideal Bayesian observer encoding and retrieving situational co-occurrence frequencies of stimuli and retrieval cues.

This model robustly reproduces a number of dissociations in recognition and recall previously used to argue for dual-process accounts of declarative memory.

We show that while it is sensible to think of recall as simply retrieving items when probed with a cue - typically the item list itself - it is better to think of recognition as retrieving cues when probed with items.

To test this theory, by manipulating the number of items and cues in a memory experiment, we show a crossover effect in memory performance within subjects such that recognition performance is superior to recall performance when the number of items is greater than the number of cues and recall performance is better than recognition when the converse holds.

We build a simple computational model around this theory, using sampling to approximate an ideal Bayesian observer encoding and retrieving situational co-occurrence frequencies of stimuli and retrieval cues.

This model robustly reproduces a number of dissociations in recognition and recall previously used to argue for dual-process accounts of declarative memory.

In contrast to prior related work, we carefully monitor the effect of the exponential number of classes in the learning guarantees as well as on the optimization complexity.

As an interesting consequence, we formalize the intuition that some task losses make learning harder than others, and that the classical 0-1 loss is ill-suited for structured prediction.

In contrast to prior related work, we carefully monitor the effect of the exponential number of classes in the learning guarantees as well as on the optimization complexity.

As an interesting consequence, we formalize the intuition that some task losses make learning harder than others, and that the classical 0-1 loss is ill-suited for structured prediction.

The standard training paradigm for these models is maximum likelihood estimation MLEor minimizing the cross-entropy of the human responses.

In contrast, discriminative dialog models D that are trained to rank a list of candidate human responses outperform their generative counterparts; in terms of automatic metrics, diversity, and informativeness of the responses.

However, D is not useful in practice since it can not be deployed to have real conversations with users.

Our work aims to achieve the best of both worlds -- the practical usefulness of G and the strong performance of D -- via knowledge transfer from D to G.

Our primary contribution is an end-to-end trainable generative visual dialog model, where G receives gradients from D as a perceptual not adversarial loss of the sequence sampled from G.

We leverage the recently proposed Gumbel-Softmax GS approximation to the discrete distribution -- specifically, a RNN is augmented with a sequence of GS samplers, which coupled with the straight-through gradient estimator enables end-to-end differentiability.

We also introduce a stronger encoder for visual dialog, and employ a self-attention mechanism for answer encoding along with a metric learning loss to aid D in better capturing semantic similarities in answer responses.

Overall, our proposed model outperforms state-of-the-art on the VisDial dataset by a significant margin 2.

The standard training paradigm for these models is maximum likelihood estimation MLEor minimizing the cross-entropy of the human responses.

In contrast, discriminative dialog models D that are trained to rank a list of candidate human responses outperform their generative counterparts; in terms of automatic metrics, diversity, and informativeness of the responses.

However, D is not useful in practice since it can not be deployed to have real conversations with users.

Our work aims to achieve the best of both worlds -- the practical usefulness of G and the strong performance of D -- via knowledge transfer from D to G.

Our primary contribution is an end-to-end trainable generative visual dialog model, where G receives gradients from D as a perceptual not adversarial loss of the sequence sampled from G.

We leverage the recently proposed Gumbel-Softmax GS approximation to the discrete distribution -- specifically, a RNN is augmented with a sequence of GS samplers, which coupled with the straight-through gradient estimator enables end-to-end differentiability.

We also introduce a stronger encoder for visual dialog, and employ a self-attention mechanism for answer encoding along with a metric learning loss to aid D in better capturing semantic similarities in answer responses.

Overall, our proposed model outperforms state-of-the-art on the VisDial dataset by a significant margin 2.

To capture the temporal coherence, in this paper, we develop MaskRNN, a recurrent neural net approach which fuses in each frame the output of two deep nets for each object instance - a binary segmentation net providing a mask and a localization net providing a bounding box.

Due to the recurrent component and the localization component, our method is able to take advantage of long-term temporal structures of the video data as well as rejecting outliers.

We validate the proposed algorithm on three challenging benchmark datasets, the DAVIS-2016 dataset, the DAVIS-2017 dataset, and the Segtrack v2 dataset, achieving state-of-the-art performance on all of them.

To capture the temporal coherence, in this paper, we develop MaskRNN, a recurrent neural net approach which fuses in each frame the output of two deep nets for each object instance - a binary segmentation net providing a mask and a localization net providing a bounding box.

Due to the recurrent component and the localization component, our method is able to take advantage of long-term temporal structures of the video data as well as rejecting outliers.

We validate the proposed algorithm on three challenging benchmark datasets, the DAVIS-2016 dataset, the DAVIS-2017 dataset, and the Segtrack v2 dataset, achieving state-of-the-art performance on all of them.

Inspired by a recently proposed model for general image classification, Recurrent Convolution Neural Network RCNNwe propose a new architecture named Gated RCNN GRCNN for solving this problem.

Its critical component, Gated Recurrent Convolution Layer GRCLis constructed by adding a gate to the Recurrent Convolution Layer RCLthe critical component of RCNN.

The gate controls the context modulation in RCL and ã«ãžããããããšèªç±ã« the feed-forward information and the recurrent information.

In addition, an efficient Bidirectional Long Short-Term Memory BLSTM is built for sequence modeling.

The GRCNN is combined with BLSTM to recognize text in natural images.

The entire GRCNN-BLSTM model can be trained end-to-end.

Experiments show that the proposed model outperforms existing methods on several benchmark datasets including the IIIT-5K, Street View Text SVT and ICDAR.

Inspired by a recently proposed model for general image classification, Recurrent Convolution Neural Network RCNNwe propose a new architecture named Gated RCNN GRCNN for solving this problem.

Its critical component, Gated Recurrent Convolution This web page GRCLis constructed by adding a gate to the Recurrent Convolution Layer RCLthe critical component of RCNN.

The gate controls the context modulation in RCL and balances the feed-forward information and the recurrent information.

In addition, an efficient Bidirectional Long Short-Term Memory BLSTM is built for sequence modeling.

The GRCNN is combined with BLSTM to recognize text in natural images.

The read article GRCNN-BLSTM model can be trained end-to-end.

Experiments show that the proposed model outperforms existing methods on several benchmark datasets including the IIIT-5K, Street View Text SVT and ICDAR.

It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bitwise operations, leading to much faster test-time inference and lower power consumption.

However, previous works on binarizing CNNs usually result in severe prediction accuracy degradation.

In this paper, we address this issue with two major innovations: 1 approximating full-precision weights with the linear combination of multiple binary weight bases; 2 employing multiple binary activations to alleviate information loss.

The implementation of the resulting binary CNN, denoted as ABC-Net, is shown to achieve much closer performance to its full-precision counterpart, and even reach the comparable prediction accuracy on ImageNet and forest trail datasets, given adequate binary weight bases and activations.

It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bitwise operations, leading to much faster test-time inference and lower power consumption.

However, previous works on binarizing CNNs usually result in severe prediction accuracy degradation.

In this paper, we address this issue with two major innovations: 1 approximating full-precision weights with the linear combination of multiple binary weight bases; 2 employing multiple binary activations to alleviate information loss.

The implementation of the resulting binary CNN, denoted as ABC-Net, is shown to achieve much closer performance to its full-precision counterpart, and even reach the comparable prediction accuracy on ImageNet and forest trail datasets, given adequate binary weight bases and activations.

As training the CNNs requires sufficiently large ground truth training data, existing approaches resort to synthetic, unrealistic datasets.

On the other hand, unsupervised methods are capable of leveraging real-world videos for training where the ground truth flow fields are not available.

These methods, however, rely on the fundamental assumptions of brightness constancy and spatial smoothness priors which do not hold near motion boundaries.

In this paper, we propose to exploit unlabeled videos for semi-supervised learning of optical flow with a Generative Adversarial Network.

Our key insight is that the adversarial loss can capture the structural patterns of flow warp errors without making explicit assumptions.

Extensive experiments on benchmark datasets demonstrate that the proposed semi-supervised algorithm performs favorably against purely supervised and semi-supervised learning schemes.

As training the CNNs requires sufficiently large ground truth training data, existing approaches resort to synthetic, unrealistic datasets.

On the other hand, unsupervised methods are capable of leveraging real-world videos for training where the ground truth flow fields are not available.

These methods, however, rely on the fundamental assumptions of brightness constancy and spatial smoothness priors which do not hold near motion boundaries.

In this paper, we propose to exploit unlabeled videos for semi-supervised learning of optical flow with a Generative Adversarial Network.

Our key insight is that the adversarial loss can capture the structural patterns of flow warp errors without making explicit assumptions.

Extensive experiments on benchmark datasets demonstrate that the proposed semi-supervised algorithm performs favorably ãã£ã³ãã£ã¯ã©ãã·ã£ãŒã²ãŒã ç¡æãªã³ã©ã€ã³ purely supervised and semi-supervised learning schemes.

In contrast to recent learning based methods for 3D reconstruction, we leverage the underlying 3D geometry of the problem through feature projection and unprojection along viewing rays.

By formulating these operations in a differentiable manner, we are able to learn the system end-to-end for the task of metric 3D reconstruction.

End-to-end learning allows us to jointly reason about shape priors while conforming to geometric constraints, enabling reconstruction from much fewer images even a single image than required by classical approaches as well as completion of unseen surfaces.

We thoroughly evaluate our approach on the ShapeNet dataset and demonstrate the benefits over classical approaches and recent learning based methods.

In contrast to recent learning based methods for 3D reconstruction, we leverage the underlying 3D geometry of the problem through feature projection and unprojection along viewing rays.

By formulating these operations in a differentiable manner, we are able to learn the system end-to-end for the task of metric 3D reconstruction.

End-to-end learning allows us to jointly reason about shape priors while conforming to geometric constraints, enabling reconstruction from much fewer images even a single image than required by classical approaches as well as completion of unseen surfaces.

We thoroughly evaluate our approach on the ShapeNet dataset and demonstrate the benefits over classical approaches and recent learning based methods.

Our results reveal that noise can make the problem considerably more difficult, with strict increases in the scaling laws even at low noise levels.

Our results reveal that noise can make the problem considerably more difficult, with strict increases in the scaling laws even at low noise levels.

Existing feed-forward based methods, while enjoying the inference efficiency, are mainly limited by inability of generalizing to unseen styles or compromised visual quality.

In this paper, we present a simple yet effective method that tackles these limitations without training on any pre-defined styles.

The key ingredient of our method is a pair of feature transforms, whitening and coloring, that are embedded to an image reconstruction network.

The whitening and coloring transforms reflect direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer.

We demonstrate the effectiveness of our algorithm by generating high-quality stylized images with comparisons to a number of recent methods.

We also analyze our method by visualizing the whitened features and synthesizing textures by simple feature coloring.

Existing feed-forward based methods, while enjoying the inference efficiency, are mainly limited by inability of generalizing to unseen styles or compromised visual quality.

In this paper, we present a simple yet effective method that tackles these limitations without training on any pre-defined styles.

The key ingredient of our method is a pair of feature transforms, whitening and coloring, that are embedded to an image reconstruction network.

The whitening and coloring transforms reflect direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer.

We demonstrate the effectiveness of our algorithm by generating high-quality stylized images with comparisons to a number of recent methods.

We also analyze our method by visualizing the whitened features and synthesizing textures by simple feature coloring.

However, we empirically found that the model shrinkage of the EPM does not typically work appropriately and leads to an overfitted solution.

In order to ensure that the model shrinkage effect of the EPM works in an appropriate manner, we proposed two novel generative constructions of the EPM: CEPM incorporating constrained gamma priors, and DEPM incorporating Dirichlet priors instead of the gamma priors.

We experimentally confirmed that the model shrinkage of the proposed models works well and that the IDEPM indicated state-of-the-art performance in generalization ability, link prediction accuracy, mixing efficiency, and convergence speed.

However, we empirically found that the model shrinkage of the EPM does not typically work appropriately and leads to an overfitted solution.

In order to ensure that the model shrinkage effect of the EPM works in an appropriate manner, we proposed see more novel generative constructions of the EPM: CEPM åŒ·çã®ãããã¯ãè§£é€ããã²ãŒã ããã66 constrained gamma priors, and DEPM incorporating Dirichlet priors instead of the gamma priors.

We experimentally confirmed that the model shrinkage of the proposed models works well and that the IDEPM indicated state-of-the-art performance in generalization ability, link prediction accuracy, mixing efficiency, and convergence speed.

In the first stage the condition image and the target pose are fed into a U-Net-like network to generate an initial but coarse image of the person with the target pose.

The second stage then refines the initial and blurry result by training a U-Net-like generator in an adversarial way.

In the first stage the condition image and the target pose are fed into a U-Net-like network to generate an initial but check this out image of the person with the target pose.

The second stage then refines the initial and blurry result by training a U-Net-like generator in an adversarial way.

Popular inference algorithms such as belief propagation BP and generalized belief propagation GBP are intimately related to linear programming LP relaxation within the Sherali-Adams hierarchy.

Despite the popularity of these algorithms, it is well understood that the Sum-of-Squares SOS hierarchy based on semidefinite programming SDP can provide superior guarantees.

In this paper, we propose binary SDP relaxations for MAP inference using the SOS hierarchy with two innovations focused on computational efficiency.

Firstly, in analogy to BP and its variants, we only introduce decision variables corresponding to contiguous regions in the graphical model.

Secondly, we solve the resulting SDP using a non-convex Burer-Monteiro style method, and develop a sequential rounding procedure.

We demonstrate that the resulting algorithm can solve problems with tens of thousands of variables within minutes, and outperforms BP and GBP on practical problems such as image denoising and Ising spin glasses.

Finally, for specific graph types, we establish a sufficient condition for the tightness of the proposed partial SOS relaxation.

Popular inference algorithms such as belief propagation BP and generalized belief propagation GBP are intimately related to linear programming LP relaxation within the Sherali-Adams hierarchy.

Despite the popularity of these algorithms, it is well understood that the Sum-of-Squares SOS hierarchy based on semidefinite programming SDP can provide superior guarantees.

In this paper, we propose binary SDP relaxations for MAP inference using the SOS hierarchy with two innovations focused on computational efficiency.

Firstly, in analogy to BP and its variants, we only introduce decision variables corresponding to contiguous regions in the graphical model.

Secondly, we solve the resulting SDP using a non-convex Burer-Monteiro style method, and develop a sequential rounding procedure.

We demonstrate that the resulting algorithm can solve problems with tens of thousands of variables within minutes, and outperforms BP and GBP on practical problems such as image denoising and Ising spin glasses.

Finally, for specific graph types, we establish a sufficient condition for the tightness of the proposed partial SOS relaxation.

While practitioners often employ variable importance methods that rely on this impurity-based information, these methods remain poorly characterized from a theoretical perspective.

We provide novel insights into the performance of these methods by deriving finite sample performance guarantees in a high-dimensional setting under various modeling assumptions.

We further demonstrate the effectiveness of these impurity-based methods via an extensive set of simulations.

While practitioners often employ variable importance methods that rely on this impurity-based information, these methods remain poorly characterized from a theoretical perspective.

We provide novel insights into the performance of these methods by deriving finite sample performance guarantees in a high-dimensional setting under various modeling assumptions.

We further demonstrate the effectiveness of these impurity-based methods via an extensive set of simulations.

The GRU is typically trained using a gradient-based method, which is subject to the exploding gradient problem in which the gradient increases significantly.

This problem is caused by an abrupt change in the dynamics of the GRU due to a small variation in the parameters.

In this paper, we find a condition under which the dynamics of the GRU changes drastically and propose a learning method to address the exploding gradient problem.

Our method constrains the dynamics of the GRU so that it does not drastically change.

We evaluated our method in experiments on language modeling and polyphonic music modeling.

Our experiments showed that our method can prevent the exploding gradient problem and improve modeling accuracy.

The GRU is typically trained using a gradient-based method, which is subject to the exploding gradient problem in which the gradient increases significantly.

This problem is caused by an abrupt change in the dynamics of the GRU due to a small variation in the parameters.

In this paper, we find a condition under which the dynamics of the GRU changes drastically and propose a learning method to address the exploding gradient problem.

Our method constrains the dynamics of the GRU so that it does not drastically change.

We evaluated our method in experiments on language modeling and polyphonic music modeling.

Our experiments showed that our method can prevent the exploding gradient problem and improve modeling accuracy.

This observation leads to many interesting results on general high-rank matrix estimation problems: 1.

This observation leads to many interesting results on general high-rank matrix estimation problems: 1.

The approach is elegant but falls short of a full description of the supervised game, and says little about the key player, the generator: for example, what does the generator actually converge to if solving the GAN game means convergence in some space of parameters?

In this paper, we unveil a broad class of distributions for which such convergence happens --- namely, deformed exponential families, a wide superset of exponential families --.

The key to our results is a variational generalization of an old theorem that relates the KL divergence between regular exponential families and divergences between their natural parameters.

The approach is elegant but falls short of a full description of the supervised game, and says little about the key player, the generator: for example, what does the generator actually converge to if solving the GAN game means convergence in some space of parameters?

In this paper, we unveil a broad class of distributions for which such convergence happens --- namely, deformed exponential families, a wide superset of exponential families --.

The key to our results is a variational generalization of an old theorem that relates the KL divergence between regular exponential families and divergences between their natural parameters.

In this work, we aim to model ããªã©ã¹ãã¬ã¹ã§æé«ã®ã¹ããã distribution of possible outputs in a conditional generative modeling setting.

The ambiguity of the mapping is distilled in a low-dimensional latent vector, which can be randomly sampled at test time.

A generator learns to map the given input, combined with this latent code, to the output.

We explicitly encourage the connection between output and the latent code to be invertible.

This helps prevent a many-to-one mapping from the latent code to the output during training, also known as the problem of mode collapse, and produces more diverse results.

We explore several variants of this approach by employing different training objectives, network architectures, and methods of injecting the latent code.

Our proposed method encourages bijective consistency between the latent encoding and output modes.

We present a systematic comparison of our method and other variants on both perceptual realism and diversity.

In this work, we aim to model a distribution of possible outputs in a conditional generative modeling setting.

The ambiguity of the mapping is distilled in a low-dimensional latent vector, which can be randomly sampled at test time.

A generator learns to map the given input, combined with this latent code, to the output.

We explicitly encourage the connection between output and the latent code to be invertible.

This helps prevent a many-to-one mapping from the latent code to the output during training, also known as the problem of mode collapse, and produces more diverse results.

We explore several variants of this approach by employing different training objectives, network architectures, and methods of injecting the latent code.

Our proposed method encourages bijective consistency between the latent encoding and output modes.

We present a systematic comparison of our method and ç¬å ç¡æé§è»ã²ãŒã variants on both perceptual realism and diversity.

However, our studies show that submatrices with different ranks could coexist in the same user-item rating matrix, so that approximations with fixed ranks cannot perfectly describe the internal structures of the rating matrix, therefore leading to inferior recommendation accuracy.

In this paper, a mixture-rank matrix approximation MRMA method is proposed, in which user-item ratings can be characterized by a mixture of LRMA models with different ranks.

Meanwhile, a learning this web page capitalizing on iterated condition modes is proposed to tackle the non-convex optimization problem pertaining to MRMA.

Experimental studies on MovieLens and Netflix datasets demonstrate that MRMA can outperform six state-of-the-art LRMA-based CF methods in terms of recommendation accuracy.

However, our studies show that submatrices with different ranks could coexist in the same user-item rating matrix, so that approximations with fixed ranks cannot perfectly describe the internal structures of the rating matrix, therefore leading to inferior recommendation accuracy.

In this paper, a mixture-rank matrix approximation MRMA method is proposed, in which user-item ratings can be characterized by a mixture of LRMA models with different ranks.

Meanwhile, a learning algorithm capitalizing on iterated condition modes is proposed to tackle the non-convex optimization problem pertaining to MRMA.

Experimental studies on MovieLens and Source datasets demonstrate that MRMA can outperform six state-of-the-art LRMA-based CF methods in terms of recommendation accuracy.

DR-submodularity captures a subclass of non-convex functions that enables both exact minimization and approximate maximization in polynomial time.

In this work we study the problem of maximizing non-monotone DR-submodular continuous functions under general down-closed convex constraints.

We start by investigating geometric properties that underlie such objectives, e.

These properties are then used to devise two optimization algorithms with provable guarantees.

This algorithm allows the use of existing methods for finding approximately stationary points as a subroutine, thus, harnessing recent progress in non-convex optimization.

Finally, we extend our approach to a broader class of generalized DR-submodular continuous functions, which captures a https://list-games-promocode.site/1/58.html spectrum of applications.

Our theoretical findings are validated on synthetic and real-world problem instances.

DR-submodularity captures a subclass of non-convex functions that enables both exact minimization and approximate maximization in polynomial time.

In this work we study the problem of maximizing non-monotone DR-submodular continuous functions under general down-closed convex constraints.

We start by investigating geometric properties that underlie such objectives, e.

These properties are then used to devise two optimization algorithms with provable guarantees.

This algorithm allows the use of existing methods for finding approximately stationary points as a subroutine, thus, harnessing recent progress in non-convex optimization.

Finally, we extend our approach to a broader class of generalized DR-submodular continuous functions, which captures a wider spectrum of applications.

Our theoretical findings are validated on synthetic and real-world problem instances.

In this paper, we look in particular at the task of learning a single visual representation that can be link utilized in the analysis of very different types of images, from dog breeds to stop signs and digits.

Inspired by recent work on learning networks that predict the parameters of another, we develop a tunable deep network architecture that, by means of adapter residual modules, can be steered on the fly to diverse visual domains.

Our method achieves a high degree of parameter sharing while maintaining or even improving the accuracy of domain-specific representations.

We also introduce the Visual Decathlon Challenge, a benchmark that evaluates the ability of representations to capture simultaneously ten very different visual domains and measures their ability to recognize well uniformly.

In this paper, we look in particular at the task of learning a single visual representation that can be successfully utilized in the analysis of very different types of images, from dog breeds to stop signs and digits.

Inspired by recent work on learning networks that predict the parameters of another, we develop a tunable deep network architecture that, by means of adapter residual modules, can be steered on the fly to diverse visual domains.

Our method achieves a high degree of parameter sharing while maintaining or even improving the accuracy of domain-specific representations.

We also introduce the Visual Decathlon Challenge, a benchmark that evaluates the ability of representations to capture simultaneously ten very different visual domains and measures their ability to recognize well uniformly.

ADMM on the dual problem is also seen to be equivalent, in the special case of two sets, with one being a linear subspace.

These connections, aside from being interesting in their own right, suggest new ways of analyzing and extending coordinate descent.

We also develop two parallel versions of coordinate descent, based on the Dykstra and ADMM connections.

ADMM on the dual problem is also seen to be equivalent, in the special case of two sets, with one being a linear subspace.

These connections, aside from being interesting in their own right, suggest new ways of analyzing and extending coordinate descent.

We also develop two parallel versions of coordinate descent, based on the Dykstra and ADMM connections.

A naive solution that repeatedly projects the viewing sphere to all tangent planes is accurate, but much too computationally intensive for real problems.

We propose to learn a spherical convolutional network that translates a planar CNN to process 360Â° imagery directly in its equirectangular projection.

Our approach learns to reproduce the flat filter outputs on 360Â° data, sensitive to the varying distortion effects across the viewing sphere.

The key benefits are 1 efficient feature extraction for 360Â° images and video, and 2 the ability to leverage powerful pre-trained networks researchers have carefully honed together with massive labeled image training sets for perspective images.

Our method yields the most accurate NBAãªã³ã©ã€ã³ã²ãŒã ã®ããŠã³ããŒã while saving orders of magnitude in computation versus the existing exact reprojection solution.

A naive solution that repeatedly projects the viewing sphere to all tangent planes is accurate, but much too computationally intensive for real problems.

We propose to learn a spherical convolutional network that translates a planar CNN to process 360Â° imagery directly in its equirectangular projection.

Our approach learns to reproduce the flat filter outputs on 360Â° data, sensitive to the varying distortion effects across the viewing sphere.

The key benefits are 1 efficient feature extraction for 360Â° images and video, and 2 the ability to leverage powerful pre-trained networks researchers have carefully honed together with massive labeled image training sets for perspective images.

Our method yields the most accurate results while saving orders of magnitude in computation versus the existing exact reprojection solution.

This introduces challenge for learning-based approaches, as 3D object annotations in real images are scarce.

Previous work chose to train on synthetic data with ground truth 3D information, but suffered from the domain adaptation issue when tested on real data.

In this work, we propose an end-to-end trainable framework, sequentially estimating 2.

Our disentangled, two-step formulation has three advantages.

First, compared to full 3D shape, 2.

Second, for 3D reconstruction from the 2.

This further relieves the domain adaptation problem.

Third, we derive differentiable projective functions from 3D shape to 2.

Our framework achieves state-of-the-art performance on 3D shape reconstruction.

This introduces challenge for learning-based approaches, as 3D object annotations in real images are scarce.

Previous work chose to train on synthetic data with ground truth 3D information, but suffered from the domain adaptation issue when tested on real data.

In this work, we propose an end-to-end trainable framework, sequentially estimating 2.

Our disentangled, two-step formulation has three advantages.

First, compared to full 3D shape, 2.

Second, for 3D reconstruction from the 2.

This further relieves the domain adaptation problem.

Third, we derive differentiable projective functions from 3D shape to 2.

Our framework achieves state-of-the-art performance on 3D shape reconstruction.

The visual question answering VQA problem is an excellent way to test such reasoning capabilities of an AI model and its multimodal representation learning.

However, the current VQA models are over-simplified deep neural networks, comprised of a long short-term memory LSTM unit for question comprehension and a convolutional neural network CNN for learning single image representation.

We argue that the single visual representation contains a limited and general information about the image contents and thus limits the model reasoning capabilities.

In this work we introduce a modular neural network model that learns a multimodal and multifaceted representation of the image and the question.

The proposed model learns to use the multimodal representation to reason about the image entities and achieves a new state-of-the-art performance on both VQA benchmark datasets, VQA v1.

The visual question answering VQA problem is an excellent way to test such reasoning capabilities of an AI model and its multimodal representation learning.

However, the current VQA models are over-simplified deep neural networks, comprised of a long short-term memory LSTM unit for question comprehension and a convolutional neural network CNN for learning single image representation.

We argue that the single visual representation contains a limited and general information about the image contents and thus limits the model reasoning capabilities.

In this work we introduce a modular neural network model that learns a multimodal and multifaceted representation of the image and the question.

The proposed model learns to use the multimodal representation to reason about the image entities and achieves a new state-of-the-art performance on both VQA benchmark datasets, VQA v1.

The absolute error is a canonical example.

Many existing methods for this task reduce to binary classification problems and employ surrogate losses, such as the hinge loss.

We instead derive uniquely defined surrogate ordinal regression loss functions by seeking the predictor that is robust to the worst-case approximations of training data labels, subject to matching certain provided training data statistics.

We demonstrate the advantages of our approach over other surrogate losses based on hinge loss approximations using UCI ordinal prediction tasks.

The absolute error is a canonical example.

Many existing methods for this task reduce to binary classification problems and employ surrogate losses, such as the hinge loss.

We instead derive uniquely defined surrogate ordinal regression loss functions by seeking the predictor that is robust to the worst-case approximations of training data labels, subject to matching certain provided training data statistics.

We demonstrate the advantages of our approach over other surrogate losses based on hinge loss approximations using UCI ordinal prediction tasks.

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