NIPS2017 papers which have [easy-to-find] arXiv preprints

nips2017-arxiv-papers.md

• Deep Subspace Clustering Network, http://arxiv.org/abs/1709.02508v1
• Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization, http://arxiv.org/abs/1707.06468v2
• Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models, http://arxiv.org/abs/1606.06841v4
• Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning, http://arxiv.org/abs/1704.02882v2
• Parametric Simplex Method for Sparse Learning, http://arxiv.org/abs/1704.01079v1
• Group Sparse Additive Machine, http://arxiv.org/abs/1206.4673v1
• The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings, http://arxiv.org/abs/1703.00864v2
• Inferring Generative Model Structure with Static Analysis, http://arxiv.org/abs/1709.02477v1
• On Structured Prediction Theory with Calibrated Convex Surrogate Losses, http://arxiv.org/abs/1703.02403v2
• Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model, http://arxiv.org/abs/1706.01554v1
• Universal Style Transfer via Feature Transforms, http://arxiv.org/abs/1705.08086v1
• Pose Guided Person Image Generation, http://arxiv.org/abs/1705.09368v4
• On the Power of Truncated SVD for General High-rank Matrix Estimation Problems, http://arxiv.org/abs/1702.06861v1
• f-GANs in an Information Geometric Nutshell, http://arxiv.org/abs/1707.04385v1
• Learning multiple visual domains with residual adapters, http://arxiv.org/abs/1705.08045v4
• Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions, http://arxiv.org/abs/1705.04768v1
• Hypothesis Transfer Learning via Transformation Functions, http://arxiv.org/abs/1612.01020v3
• Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization, http://arxiv.org/abs/1703.00439v3
• Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks, http://arxiv.org/abs/1703.04379v3
• Efficient Online Linear Optimization with Approximation Algorithms, http://arxiv.org/abs/1709.03093v1
• Geometric Descent Method for Convex Composite Minimization, http://arxiv.org/abs/1612.09034v4
• Nonparametric Online Regression while Learning the Metric, http://arxiv.org/abs/1705.07853v1
• Safe and Nested Subgame Solving for Imperfect-Information Games, http://arxiv.org/abs/1705.02955v2
• Unsupervised Image-to-Image Translation Networks, http://arxiv.org/abs/1703.00848v1
• Improved Dynamic Regret for Non-degeneracy Functions, http://arxiv.org/abs/1608.03933v1
• Deep Mean-Shift Priors for Image Restoration, http://arxiv.org/abs/1709.03749v1
• Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees, http://arxiv.org/abs/1705.11041v2
• Label Distribution Learning Forests, http://arxiv.org/abs/1702.06086v2
• Unsupervised object learning from dense equivariant image labelling, http://arxiv.org/abs/1706.02932v1
• Safe Model-based Reinforcement Learning with Stability Guarantees, http://arxiv.org/abs/1705.08551v1
• Online multiclass boosting, http://arxiv.org/abs/1702.07305v2
• GP CaKe: Effective brain connectivity with causal kernels, http://arxiv.org/abs/1705.05603v1
• Decoupling "when to update" from "how to update", http://arxiv.org/abs/1706.02613v1
• Learning to Pivot with Adversarial Networks, http://arxiv.org/abs/1611.01046v3
• Active Bias: Training a More Accurate Neural Network by Emphasizing High Variance Samples, http://arxiv.org/abs/1704.07433v2
• Inductive Representation Learning on Large Graphs, http://arxiv.org/abs/1706.02216v1
• Gradient Descent Can Take Exponential Time to Escape Saddle Points, http://arxiv.org/abs/1705.10412v1
• Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction, http://arxiv.org/abs/1705.07585v1
• Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding, http://arxiv.org/abs/1705.08006v2
• Integration Methods and Optimization Algorithms, http://arxiv.org/abs/1702.06751v1
• Sharpness, Restart and Acceleration, http://arxiv.org/abs/1702.03828v1
• Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations, http://arxiv.org/abs/1704.00648v2
• Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results, http://arxiv.org/abs/1703.01780v2
• Matching neural paths: transfer from recognition to correspondence search, http://arxiv.org/abs/1705.08272v2
• Linearly constrained Gaussian processes, http://arxiv.org/abs/1703.00787v1
• Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets, http://arxiv.org/abs/1705.10479v1
• Online Prediction with Selfish Experts, http://arxiv.org/abs/1702.03615v2
• Learning Unknown Markov Decision Processes: A Thompson Sampling Approach, http://arxiv.org/abs/1709.04570v1
• Testing and Learning on Distributions with Symmetric Noise Invariance, http://arxiv.org/abs/1703.07596v1
• A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering, http://arxiv.org/abs/1701.09177v4
• Accelerated consensus via Min-Sum Splitting, http://arxiv.org/abs/1706.03807v1
• Nonbacktracking Bounds on the Influence in Independent Cascade Models, http://arxiv.org/abs/1706.05295v2
• Learning with Feature Evolvable Streams, http://arxiv.org/abs/1706.05259v1
• Linear regression without correspondence, http://arxiv.org/abs/1705.07048v1
• Probabilistic Rule Realization and Selection, http://arxiv.org/abs/1709.01674v1
• Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions, http://arxiv.org/abs/1705.08184v1
• A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis, http://arxiv.org/abs/1703.08937v1
• Learning Multiple Tasks with Deep Relationship Networks, http://arxiv.org/abs/1506.02117v3
• Online to Offline Conversions and Adaptive Minibatch Sizes, http://arxiv.org/abs/1705.10499v2
• Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure, http://arxiv.org/abs/1610.00970v5
• Deep Learning with Topological Signatures, http://arxiv.org/abs/1707.04041v1
• Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues, http://arxiv.org/abs/1705.08430v2
• What-If Reasoning using Counterfactual Gaussian Processes, http://arxiv.org/abs/1703.10651v2
• Train longer, generalize better: closing the generalization gap in large batch training of neural networks, http://arxiv.org/abs/1705.08741v1
• Adaptive Clustering through Semidefinite Programming, http://arxiv.org/abs/1705.06615v1
• The Numerics of GANs, http://arxiv.org/abs/1705.10461v1
• Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search, http://arxiv.org/abs/1705.04405v1
• Revenue Optimization with Approximate Bid Predictions, http://arxiv.org/abs/1706.04732v1
• Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization, http://arxiv.org/abs/1702.08651v1
• Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models, http://arxiv.org/abs/1702.03275v2
• Generating steganographic images via adversarial training, http://arxiv.org/abs/1703.00371v3
• Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration, http://arxiv.org/abs/1705.09634v1
• PixelGAN Autoencoders, http://arxiv.org/abs/1706.00531v1
• Consistent Multitask Learning with Nonlinear Output Relations, http://arxiv.org/abs/1705.08118v2
• Stabilizing Training of Generative Adversarial Networks through Regularization, http://arxiv.org/abs/1705.09367v1
• First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization, http://arxiv.org/abs/1709.00599v1
• Bayesian Dyadic Trees and Histograms for Regression, http://arxiv.org/abs/1708.00078v1
• Elementary Symmetric Polynomials for Optimal Experimental Design, http://arxiv.org/abs/1705.09677v1
• Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols, http://arxiv.org/abs/1705.11192v1
• Backprop without Learning Rates Through Coin Betting, http://arxiv.org/abs/1705.07795v2
• Pixels to Graphs by Associative Embedding, http://arxiv.org/abs/1706.07365v1
• MMD GAN: Towards Deeper Understanding of Moment Matching Network, http://arxiv.org/abs/1705.08584v1
• The Reversible Residual Network: Backpropagation Without Storing Activations, http://arxiv.org/abs/1707.04585v1
• Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe, http://arxiv.org/abs/1702.06917v2
• Expectation Propagation for t-Exponential Family Using Q-Algebra, http://arxiv.org/abs/1705.09046v2
• Few-Shot Learning Through an Information Retrieval Lens, http://arxiv.org/abs/1707.02610v1
• Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation, http://arxiv.org/abs/1705.08475v1
• Associative Embedding: End-to-End Learning for Joint Detection and Grouping, http://arxiv.org/abs/1611.05424v2
• Practical Locally Private Heavy Hitters, http://arxiv.org/abs/1707.04982v1
• Differentiable Learning of Logical Rules for Knowledge Base Reasoning, http://arxiv.org/abs/1702.08367v2
• Masked Autoregressive Flow for Density Estimation, http://arxiv.org/abs/1705.07057v1
• Non-convex Finite-Sum Optimization Via SCSG Methods, http://arxiv.org/abs/1706.09156v1
• Fast Black-box Variational Inference through Stochastic Trust-Region Optimization, http://arxiv.org/abs/1706.02375v1
• SGD Learns the Conjugate Kernel Class of the Network, http://arxiv.org/abs/1702.08503v2
• Noise-Tolerant Interactive Learning Using Pairwise Comparisons, http://arxiv.org/abs/1704.05820v2
• Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems, http://arxiv.org/abs/1709.04482v1
• Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications, http://arxiv.org/abs/1705.11107v1
• Fisher GAN, http://arxiv.org/abs/1705.09675v2
• Information-theoretic analysis of generalization capability of learning algorithms, http://arxiv.org/abs/1705.07809v1
• Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems , http://arxiv.org/abs/1706.06054v1
• Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM, http://arxiv.org/abs/1705.10829v1
• Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network, http://arxiv.org/abs/1709.04555v1
• Parallel Streaming Wasserstein Barycenters, http://arxiv.org/abs/1705.07443v1
• Dual Discriminator Generative Adversarial Nets, http://arxiv.org/abs/1709.03831v1
• On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning, http://arxiv.org/abs/1706.06066v1
• Bridging the Gap Between Value and Policy Based Reinforcement Learning, http://arxiv.org/abs/1702.08892v2
• Alternating Estimation for Structured High-Dimensional Multi-Response Models, http://arxiv.org/abs/1606.08957v1
• Decomposable Submodular Function Minimization: Discrete and Continuous, http://arxiv.org/abs/1703.01830v1
• Gauging Variational Inference, http://arxiv.org/abs/1703.01056v5
• Beyond Parity: Fairness Objectives for Collaborative Filtering, http://arxiv.org/abs/1705.08804v1
• Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach, http://arxiv.org/abs/1707.06334v2
• Deep Voice 2: Multi-Speaker Neural Text-to-Speech, http://arxiv.org/abs/1705.08947v1
• Continual Learning with Deep Generative Replay, http://arxiv.org/abs/1705.08690v2
• Learning Causal Structures Using Regression Invariance, http://arxiv.org/abs/1705.09644v1
• Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback, http://arxiv.org/abs/1605.06593v2
• Reinforcement Learning under Model Mismatch, http://arxiv.org/abs/1706.04711v1
• Hierarchical Attentive Recurrent Tracking, http://arxiv.org/abs/1706.09262v2
• Rotting Bandits, http://arxiv.org/abs/1702.07274v3
• Unbiased estimates for linear regression via volume sampling, http://arxiv.org/abs/1705.06908v3
• Stein Variational Gradient Descent as Gradient Flow, http://arxiv.org/abs/1704.07520v1
• Shallow Updates for Deep Reinforcement Learning, http://arxiv.org/abs/1705.07461v1
• Adversarial Ranking for Language Generation, http://arxiv.org/abs/1705.11001v1
• Generalization Properties of Learning with Random Features, http://arxiv.org/abs/1602.04474v3
• Differentially private Bayesian learning on distributed data, http://arxiv.org/abs/1703.01106v2
• Learning to Compose Domain-Specific Transformations for Data Augmentation, http://arxiv.org/abs/1709.01643v1
• Wasserstein Learning of Deep Generative Point Process Models, http://arxiv.org/abs/1705.08051v1
• Streaming Sparse Gaussian Process Approximations, http://arxiv.org/abs/1705.07131v1
• A Regularized Framework for Sparse and Structured Neural Attention, http://arxiv.org/abs/1705.07704v1
• Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes, http://arxiv.org/abs/1704.02801v2
• Certified Defenses for Data Poisoning Attacks, http://arxiv.org/abs/1706.03691v1
• Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization, http://arxiv.org/abs/1706.01686v1
• Unsupervised Sequence Classification using Sequential Output Statistics, http://arxiv.org/abs/1702.07817v2
• Concrete Dropout, http://arxiv.org/abs/1705.07832v1
• A multi-agent reinforcement learning model of common-pool resource appropriation, http://arxiv.org/abs/1707.06600v2
• Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks, http://arxiv.org/abs/1704.06803v1
• Reducing Reparameterization Gradient Variance, http://arxiv.org/abs/1705.07880v1
• Joint distribution optimal transportation for domain adaptation, http://arxiv.org/abs/1705.08848v1
• Multiresolution Kernel Approximation for Gaussian Process Regression, http://arxiv.org/abs/1708.02183v1
• Diving into the shallows: a computational perspective on large-scale shallow learning, http://arxiv.org/abs/1703.10622v2
• Inferring The Latent Structure of Human Decision-Making from Raw Visual Inputs, http://arxiv.org/abs/1703.08840v1
• Recursive Sampling for the Nystrom Method, http://arxiv.org/abs/1605.07583v4
• Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning, http://arxiv.org/abs/1706.00387v1
• Conservative Contextual Linear Bandits, http://arxiv.org/abs/1611.06426v2
• On-the-fly Operation Batching in Dynamic Computation Graphs, http://arxiv.org/abs/1705.07860v1
• Nonlinear Acceleration of Stochastic Algorithms, http://arxiv.org/abs/1706.07270v2
• Streaming Weak Submodularity: Interpreting Neural Networks on the Fly, http://arxiv.org/abs/1703.02647v2
• Counterfactual Fairness, http://arxiv.org/abs/1703.06856v2
• Triple Generative Adversarial Nets, http://arxiv.org/abs/1703.02291v3
• The Marginal Value of Adaptive Gradient Methods in Machine Learning, http://arxiv.org/abs/1705.08292v1
• Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification, http://arxiv.org/abs/1701.06511v3
• Deconvolutional Paragraph Representation Learning, http://arxiv.org/abs/1708.04729v2
• Efficient Use of Limited-Memory Resources to Accelerate Linear Learning, http://arxiv.org/abs/1708.05357v1
• Sobolev Training for Neural Networks, http://arxiv.org/abs/1706.04859v3
• Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach, http://arxiv.org/abs/1705.07086v1
• Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication, http://arxiv.org/abs/1705.10464v1
• Unsupervised Learning of Disentangled Representations from Video, http://arxiv.org/abs/1705.10915v1
• Improved Graph Laplacian via Geometric Self-Consistency, http://arxiv.org/abs/1406.0118v1
• Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions, http://arxiv.org/abs/1703.02567v4
• Trimmed Density Ratio Estimation, http://arxiv.org/abs/1703.03216v2
• Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems, http://arxiv.org/abs/1609.09059v2
• Doubly Stochastic Variational Inference for Deep Gaussian Processes, http://arxiv.org/abs/1705.08933v1
• Overcoming Catastrophic Forgetting by Incremental Moment Matching, http://arxiv.org/abs/1703.08475v2
• Balancing information exposure in social networks, http://arxiv.org/abs/1709.01491v1
• Query Complexity of Clustering with Side Information, http://arxiv.org/abs/1706.07719v1
• Robust Optimization for Non-Convex Objectives, http://arxiv.org/abs/1707.01047v1
• Adaptive Classification for Prediction Under a Budget, http://arxiv.org/abs/1705.10194v1
• Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization , http://arxiv.org/abs/1706.05737v1
• A unified approach to interpreting model predictions, http://arxiv.org/abs/1705.07874v1
• Stochastic Approximation for Canonical Correlation Analysis, http://arxiv.org/abs/1702.06818v1
• Scalable Variational Inference for Dynamical Systems, http://arxiv.org/abs/1705.07079v1
• Working hard to know your neighbor's margins: Local descriptor learning loss, http://arxiv.org/abs/1705.10872v2
• Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex, http://arxiv.org/abs/1702.07842v2
• Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon, http://arxiv.org/abs/1705.07565v1
• Monte-Carlo Tree Search by Best Arm Identification, http://arxiv.org/abs/1706.02986v1
• Hash Embeddings for Efficient Word Representations, http://arxiv.org/abs/1709.03933v1
• A simple neural network module for relational reasoning, http://arxiv.org/abs/1706.01427v1
• Active Exploration for Learning Symbolic Representations, http://arxiv.org/abs/1709.01490v1
• Polynomial time algorithms for dual volume sampling, http://arxiv.org/abs/1703.02674v2
• Hindsight Experience Replay, http://arxiv.org/abs/1707.01495v2
• Teaching Machines to Describe Images with Natural Language Feedback, http://arxiv.org/abs/1706.00130v2
• Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes, http://arxiv.org/abs/1705.09862v1
• K-Medoids For K-Means Seeding, http://arxiv.org/abs/1609.04723v4
• Robust Imitation of Diverse Behaviors, http://arxiv.org/abs/1707.02747v2
• Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent, http://arxiv.org/abs/1705.09056v5
• A Sample Complexity Measure with Applications to Learning Optimal Auctions, http://arxiv.org/abs/1704.02598v2
• Thinking Fast and Slow with Deep Learning and Tree Search, http://arxiv.org/abs/1705.08439v1
• Hybrid Reward Architecture for Reinforcement Learning, http://arxiv.org/abs/1706.04208v1
• A Greedy Approach for Budgeted Maximum Inner Product Search, http://arxiv.org/abs/1610.03317v1
• Towards Understanding Adversarial Learning for Joint Distribution Matching, http://arxiv.org/abs/1709.01215v1
• Improved Semi-supervised Learning with GANs using Manifold Invariances, http://arxiv.org/abs/1705.08850v1
• Approximation and Convergence Properties of Generative Adversarial Learning, http://arxiv.org/abs/1705.08991v1
• From Bayesian Sparsity to Gated Recurrent Nets, http://arxiv.org/abs/1706.02815v2
• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?, http://arxiv.org/abs/1703.04977v1
• Gradient descent GAN optimization is locally stable, http://arxiv.org/abs/1706.04156v1
• Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks, http://arxiv.org/abs/1706.00038v1
• Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model, http://arxiv.org/abs/1706.03458v1
• Do Deep Neural Networks Suffer from Crowding?, http://arxiv.org/abs/1706.08616v1
• Learning from Complementary Labels, http://arxiv.org/abs/1705.07541v1
• Clustering with Noisy Queries, http://arxiv.org/abs/1706.07510v1
• Training Quantized Nets: A Deeper Understanding, http://arxiv.org/abs/1706.02379v2
• Permutation-based Causal Inference Algorithms with Interventions, http://arxiv.org/abs/1705.10220v1
• Gradient Methods for Submodular Maximization, http://arxiv.org/abs/1708.03949v2
• Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos, http://arxiv.org/abs/1703.01138v1
• Collaborative Deep Learning in Fixed Topology Networks, http://arxiv.org/abs/1706.07880v1
• Learning Disentangled Representations with Semi-Supervised Deep Generative Models, http://arxiv.org/abs/1706.00400v1
• A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control, http://arxiv.org/abs/1706.05378v1
• Early stopping for kernel boosting algorithms: A general analysis with localized complexities, http://arxiv.org/abs/1707.01543v1
• Gaussian Quadrature for Kernel Features, http://arxiv.org/abs/1709.02605v1
• Value Prediction Network, http://arxiv.org/abs/1707.03497v1
• Implicit Regularization in Matrix Factorization, http://arxiv.org/abs/1705.09280v1
• Asynchronous Coordinate Descent under More Realistic Assumptions, http://arxiv.org/abs/1705.08494v2
• Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls, http://arxiv.org/abs/1708.02105v1
• Invariance and Stability of Deep Convolutional Representations, http://arxiv.org/abs/1706.03078v1
• Statistical Cost Sharing, http://arxiv.org/abs/1703.03111v1
• The Expressive Power of Neural Networks: A View from the Width, http://arxiv.org/abs/1709.02540v1
• Spectrally-normalized margin bounds for neural networks, http://arxiv.org/abs/1706.08498v1
• Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes, http://arxiv.org/abs/1706.06544v1
• Scalable Planning with Tensorflow for Hybrid Nonlinear Domains, http://arxiv.org/abs/1704.07511v2
• Boltzmann Exploration Done Right, http://arxiv.org/abs/1705.10257v1
• Poincaré Embeddings for Learning Hierarchical Representations, http://arxiv.org/abs/1705.08039v2
• Learning Combinatorial Optimization Algorithms over Graphs, http://arxiv.org/abs/1704.01665v3
• Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles, http://arxiv.org/abs/1612.01474v2
• Causal Effect Inference with Deep Latent Variable Models, http://arxiv.org/abs/1705.08821v1
• Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity, http://arxiv.org/abs/1703.01196v1
• Gradient Episodic Memory for Continuum Learning, http://arxiv.org/abs/1706.08840v4
• Good Semi-supervised Learning That Requires a Bad GAN, http://arxiv.org/abs/1705.09783v2
• Towards Generalization and Simplicity in Continuous Control, http://arxiv.org/abs/1703.02660v1
• Modulating early visual processing by language, http://arxiv.org/abs/1707.00683v1
• Learning Mixture of Gaussians with Streaming Data, http://arxiv.org/abs/1707.02391v1
• Approximation Algorithms for $\ell_0$-Low Rank Approximation, http://arxiv.org/abs/1705.06730v1
• Structured Bayesian Pruning via Log-Normal Multiplicative Noise, http://arxiv.org/abs/1705.07283v1
• Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin, http://arxiv.org/abs/1708.00339v2
• Acceleration and Averaging in Stochastic Descent Dynamics, http://arxiv.org/abs/1707.06219v1
• Kernel functions based on triplet comparisons, http://arxiv.org/abs/1607.08456v1
• An Error Detection and Correction Framework for Connectomics, http://arxiv.org/abs/1708.02599v1
• Style Transfer from Non-parallel Text by Cross-Alignment, http://arxiv.org/abs/1705.09655v1
• Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference, http://arxiv.org/abs/1703.09194v3
• Kernel Feature Selection via Conditional Covariance Minimization, http://arxiv.org/abs/1707.01164v1
• Statistical Convergence Analysis of Gradient EM on General Gaussian Mixture Models, http://arxiv.org/abs/1705.08530v1
• Real Time Image Saliency for Black Box Classifiers, http://arxiv.org/abs/1705.07857v1
• Stochastic Mirror Descent for Non-Convex Optimization, http://arxiv.org/abs/1705.02031v1