Downloads 2018

Format:
Posters:
Tutorials:
Invited talks:
Workshops:
Demonstrations:

Number of events: 541

3D-FilterMap: A Compact Architecture for Deep Convolutional Neural Networks
3D-Scene-GAN: Three-dimensional Scene Reconstruction with Generative Adversarial Networks
A Bayesian Perspective on Generalization and Stochastic Gradient Descent
Accelerating Neural Architecture Search using Performance Prediction
A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs
Action-dependent Control Variates for Policy Optimization via Stein Identity
Activation Maximization Generative Adversarial Nets
Active Learning for Convolutional Neural Networks: A Core-Set Approach
Active Neural Localization
Adapting to Continuously Shifting Domains
Adaptive Dropout with Rademacher Complexity Regularization
Adaptive Memory Networks
Adaptive Path-Integral Approach for Representation Learning and Planning
Adaptive Quantization of Neural Networks
A Dataset To Evaluate The Representations Learned By Video Prediction Models
Additive Margin Softmax for Face Verification
A Deep Reinforced Model for Abstractive Summarization
A differentiable BLEU loss. Analysis and first results
A DIRT-T Approach to Unsupervised Domain Adaptation
Adversarial Dropout Regularization
Adversarial Policy Gradient for Alternating Markov Games
Adversarial Spheres
A Flexible Approach to Automated RNN Architecture Generation
A Framework for the Quantitative Evaluation of Disentangled Representations
A Hierarchical Model for Device Placement
A Language and Compiler View on Differentiable Programming
All-but-the-Top: Simple and Effective Postprocessing for Word Representations
Alternating Multi-bit Quantization for Recurrent Neural Networks
AmbientGAN: Generative models from lossy measurements
A moth brain learns to read MNIST
Analysis of Cosmic Microwave Background with Deep Learning
Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies
An efficient framework for learning sentence representations
A Neural Network Model That Can Reason
A Neural Representation of Sketch Drawings
An Evaluation of Fisher Approximations Beyond Kronecker Factorization
A New Method of Region Embedding for Text Classification
An Experimental Study of Neural Networks for Variable Graphs
An image representation based convolutional network for DNA classification
An interpretable LSTM neural network for autoregressive exogenous model
An Online Learning Approach to Generative Adversarial Networks
An Optimization View on Dynamic Routing Between Capsules
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy
A Proximal Block Coordinate Descent Algorithm for Deep Neural Network Training
Are Efficient Deep Representations Learnable?
A Scalable Laplace Approximation for Neural Networks
A Simple Neural Attentive Meta-Learner
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
Aspect-based Question Generation
Attacking Binarized Neural Networks
Attacking the Madry Defense Model with $L_1$-based Adversarial Examples
Augmenting Clinical Intellgence with Machine Intelligence
Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis
Auto-Encoding Sequential Monte Carlo
AUTOMATED DESIGN USING NEURAL NETWORKS AND GRADIENT DESCENT
Automatically Inferring Data Quality for Spatiotemporal Forecasting
Autoregressive Generative Adversarial Networks
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation
Bayesian Incremental Learning for Deep Neural Networks
Benefits of Depth for Long-Term Memory of Recurrent Networks
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling
Black-box Attacks on Deep Neural Networks via Gradient Estimation
Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions
Boosting the Actor with Dual Critic
Boundary Seeking GANs
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
Building Generalizable Agents with a Realistic and Rich 3D Environment
Can Deep Reinforcement Learning solve Erdos-Selfridge-Spencer Games?
Can Neural Networks Understand Logical Entailment?
Can recurrent neural networks warp time?
Capturing Human Category Representations by Sampling in Deep Feature Spaces
Cascade Adversarial Machine Learning Regularized with a Unified Embedding
Causal Discovery Using Proxy Variables
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
Censoring Representations with Multiple-Adversaries over Random Subspaces
Certified Defenses against Adversarial Examples
Certifying Some Distributional Robustness with Principled Adversarial Training
cGANs with Projection Discriminator
Challenges in Disentangling Independent Factors of Variation
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
ChatPainter: Improving Text to Image Generation using Dialogue
Clustering Meets Implicit Generative Models
COLD FUSION: TRAINING SEQ2SEQ MODELS TOGETHER WITH LANGUAGE MODELS
Combating Adversarial Attacks Using Sparse Representations
Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs
ComboGAN: Unrestricted Scalability for Image Domain Translation
Communication Algorithms via Deep Learning
Comparing Fixed and Adaptive Computation Time for Recurrent Neural Networks
Compositional Attention Networks for Machine Reasoning
Compositional Obverter Communication Learning from Raw Visual Input
Compressing Word Embeddings via Deep Compositional Code Learning
Compression by the signs: distributed learning is a two-way street
Concept Learning with Energy-Based Models
Conditional Networks for Few-Shot Semantic Segmentation
Consequentialist conditional cooperation in social dilemmas with imperfect information
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
Convolutional Sequence Modeling Revisited
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
Countering Adversarial Images using Input Transformations
Coupled Ensembles of Neural Networks
Covariant Compositional Networks For Learning Graphs
Critical Percolation as a Framework to Analyze the Training of Deep Networks
Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties
DCN+: Mixed Objective And Deep Residual Coattention for Question Answering
Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
Decision Boundary Analysis of Adversarial Examples
Decoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity Tasks
Decoupling Dynamics and Reward for Transfer Learning
Decoupling the Layers in Residual Networks
Deep Active Learning for Named Entity Recognition
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
Deep Complex Networks
Deep Convolutional Malware Classifiers Can Learn from Raw Executables and Labels Only
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design
Deep Learning as a Mixed Convex-Combinatorial Optimization Problem
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
Deep learning mutation prediction enables early stage lung cancer detection in liquid biopsy
Deep Learning with Ensembles of Neocortical Microcircuits
Deep Learning with Logged Bandit Feedback
DeepNCM: Deep Nearest Class Mean Classifiers
Deep Neural Maps
Deep Neural Networks as Gaussian Processes
Deep Rewiring: Training very sparse deep networks
Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models
Demystifying MMD GANs
Depth separation and weight-width trade-offs for sigmoidal neural networks
Depthwise Separable Convolutions for Neural Machine Translation
Designing Efficient Neural Attention Systems Towards Achieving Human-level Sharp Vision
Detecting Statistical Interactions from Neural Network Weights
DiCE: The Infinitely Differentiable Monte-Carlo Estimator
Differentiable Neural Network Architecture Search
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
Distributed Distributional Deterministic Policy Gradients
Distributed Fine-tuning of Language Models on Private Data
Distributed Prioritized Experience Replay
Distributional Adversarial Networks
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
Divide and Conquer Networks
Divide-and-Conquer Reinforcement Learning
DLVM: A modern compiler infrastructure for deep learning systems
DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images
Do GANs learn the distribution? Some Theory and Empirics
Don't Decay the Learning Rate, Increase the Batch Size
DORA The Explorer: Directed Outreaching Reinforcement Action-Selection
Dynamic Neural Program Embeddings for Program Repair
Easing non-convex optimization with neural networks
eCommerceGAN: A Generative Adversarial Network for e-commerce
Efficient Entropy For Policy Gradient with Multi-Dimensional Action Space
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs
Efficient Sparse-Winograd Convolutional Neural Networks
Eigenoption Discovery through the Deep Successor Representation
Emergence of grid-like representations by training recurrent neural networks to perform spatial localization
Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input
Emergent Communication in a Multi-Modal, Multi-Step Referential Game
Emergent Communication through Negotiation
Emergent Complexity via Multi-Agent Competition
Emergent Translation in Multi-Agent Communication
Empirical Analysis of the Hessian of Over-Parametrized Neural Networks
Empirical Risk Landscape Analysis for Understanding Deep Neural Networks
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Ensemble Adversarial Training: Attacks and Defenses
Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms
Espresso: Efficient Forward Propagation for Binary Deep Neural Networks
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
Evaluating visual "common sense" using fine-grained classification and captioning tasks
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
Expert-based reward function training: the novel method to train sequence generators
Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design
Exponentially vanishing sub-optimal local minima in multilayer neural networks
Expressive power of recurrent neural networks
Extending Robust Adversarial Reinforcement Learning Considering Adaptation and Diversity
Extending the Framework of Equilibrium Propagation to General Dynamics
Fast and Accurate Reading Comprehension by Combining Self-Attention and Convolution
Fast and Accurate Text Classification: Skimming, Rereading and Early Stopping
Faster Discovery of Neural Architectures by Searching for Paths in a Large Model
Faster Neural Networks Straight from JPEG
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
Fast Node Embeddings: Learning Ego-Centric Representations
FearNet: Brain-Inspired Model for Incremental Learning
Feature-Based Metrics for Exploring the Latent Space of Generative Models
Feature Incay for Representation Regularization
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions
Few-Shot Learning with Graph Neural Networks
Fidelity-Weighted Learning
FigureQA: An Annotated Figure Dataset for Visual Reasoning
Finding Flatter Minima with SGD
Fireside Chat with Daphne Koller
Fix your classifier: the marginal value of training the last weight layer
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
Fraternal Dropout
From Generative Models to Generative Agents
FusionNet: Fusing via Fully-aware Attention with Application to Machine Comprehension
GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets
Gaussian Process Behaviour in Wide Deep Neural Networks
Generalizing Across Domains via Cross-Gradient Training
Generalizing Hamiltonian Monte Carlo with Neural Networks
Generating Natural Adversarial Examples
Generating Wikipedia by Summarizing Long Sequences
Generative Modeling for Protein Structures
Generative Models of Visually Grounded Imagination
Generative networks as inverse problems with Scattering transforms
GeoSeq2Seq: Information Geometric Sequence-to-Sequence Networks
GILBO: One Metric to Measure Them All
GitGraph - from Computational Subgraphs to Smaller Architecture Search Spaces
Global Optimality Conditions for Deep Neural Networks
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
Gradient-based Optimization of Neural Network Architecture
Gradient Estimators for Implicit Models
Gradients explode - Deep Networks are shallow - ResNet explained
Graph Attention Networks
Graph Partition Neural Networks for Semi-Supervised Classification
Guide Actor-Critic for Continuous Control
HexaConv
Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning
Hierarchical Density Order Embeddings
Hierarchical Representations for Efficient Architecture Search
Hierarchical Subtask Discovery with Non-Negative Matrix Factorization
Hockey-Stick GAN
HoME: a Household Multimodal Environment
Hyperparameter optimization: a spectral approach
IamNN: Iterative and Adaptive Mobile Neural Network for efficient image classification
Identifying Analogies Across Domains
Imitation Learning from Visual Data with Multiple Intentions
Implicit Causal Models for Genome-wide Association Studies
Improving GANs Using Optimal Transport
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization
Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect
Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction
Inference in probabilistic graphical models by Graph Neural Networks
Initialization matters: Orthogonal Predictive State Recurrent Neural Networks
In reinforcement learning, all objective functions are not equal
Interactive Grounded Language Acquisition and Generalization in a 2D World
Interpretable Counting for Visual Question Answering
Intriguing Properties of Adversarial Examples
Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play
Investigating Human Priors for Playing Video Games
i-RevNet: Deep Invertible Networks
Isolating Sources of Disentanglement in Variational Autoencoders
Iterative GANs for Rotating Visual Objects
Jointly Learning "What" and "How" from Instructions and Goal-States
Kernel Implicit Variational Inference
Kronecker-factored Curvature Approximations for Recurrent Neural Networks
Kronecker Recurrent Units
Large scale distributed neural network training through online distillation
Large Scale Optimal Transport and Mapping Estimation
Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models
Latent Space Oddity: on the Curvature of Deep Generative Models
Learning a Generative Model for Validity in Complex Discrete Structures
LEARNING AND ANALYZING VECTOR ENCODING OF SYMBOLIC REPRESENTATION
Learning and Memorization
Learning an Embedding Space for Transferable Robot Skills
Learning a neural response metric for retinal prosthesis
Learning Approximate Inference Networks for Structured Prediction
Learning Awareness Models
Learning Causal Mechanisms
Learning Deep Mean Field Games for Modeling Large Population Behavior
Learning Deep Models: Critical Points and Local Openness
Learning Differentially Private Recurrent Language Models
Learning Discrete Weights Using the Local Reparameterization Trick
Learning Disentangled Representations with Wasserstein Auto-Encoders
Learning Efficient Tensor Representations with Ring Structure Networks
Learning from Between-class Examples for Deep Sound Recognition
Learning From Noisy Singly-labeled Data
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
Learning How Not to Act in Text-based Games
Learning how to explain neural networks: PatternNet and PatternAttribution
Learning Intrinsic Sparse Structures within Long Short-Term Memory
Learning Invariances for Policy Generalization
Learning Invariance with Compact Transforms
Learning Latent Permutations with Gumbel-Sinkhorn Networks
Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization
Learning Longer-term Dependencies in RNNs with Auxiliary Losses
Learning One-hidden-layer Neural Networks with Landscape Design
Learning Parametric Closed-Loop Policies for Markov Potential Games
Learning Representations and Generative Models for 3D Point Clouds
Learning Rich Image Representation with Deep Layer Aggregation
Learning Robust Rewards with Adverserial Inverse Reinforcement Learning
Learning Sparse Latent Representations with the Deep Copula Information Bottleneck
Learning Sparse Neural Networks through L_0 Regularization
Learning to cluster in order to transfer across domains and tasks
Learning to Count Objects in Natural Images for Visual Question Answering
Learning to Infer
Learning to Learn Without Labels
Learning to Multi-Task by Active Sampling
Learning to Organize Knowledge with N-Gram Machines
Learning to Represent Programs with Graphs
LEARNING TO SHARE: SIMULTANEOUS PARAMETER TYING AND SPARSIFICATION IN DEEP LEARNING
Learning to Teach
Learning via social awareness: improving sketch representations with facial feedback
Learning Wasserstein Embeddings
Learn to Pay Attention
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning
Leveraging Constraint Logic Programming for Neural Guided Program Synthesis
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis
Lifelong Learning with Dynamically Expandable Networks
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values
Loss-aware Weight Quantization of Deep Networks
LSH-SAMPLING BREAKS THE COMPUTATIONAL CHICKEN-AND-EGG LOOP IN ADAPTIVE STOCHASTIC GRADIENT ESTIMATION
LSTM Iteration Networks: An Exploration of Differentiable Path Finding
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
MaskGAN: Better Text Generation via Filling in the _______
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent
Matrix capsules with EM routing
Maximum a Posteriori Policy Optimisation
Measuring the Intrinsic Dimension of Objective Landscapes
MemCNN: a Framework for Developing Memory Efficient Deep Invertible Networks
Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks
Memory Architectures in Recurrent Neural Network Language Models
Memory Augmented Control Networks
Memory-based Parameter Adaptation
Meta-Learning a Dynamical Language Model
Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm
Meta-Learning for Batch Mode Active Learning
Meta-Learning for Semi-Supervised Few-Shot Classification
META LEARNING SHARED HIERARCHIES
MGAN: Training Generative Adversarial Nets with Multiple Generators
Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation
Minimally Redundant Laplacian Eigenmaps
Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity
Mitigating Adversarial Effects Through Randomization
Mixed Precision Training
Mixed Precision Training of Convolutional Neural Networks using Integer Operations
mixup: Beyond Empirical Risk Minimization
Model compression via distillation and quantization
Model-Ensemble Trust-Region Policy Optimization
Modular Continual Learning in a Unified Visual Environment
Monotonic Chunkwise Attention
Monotonic models for real-time dynamic malware detection
Multi-Agent Generative Adversarial Imitation Learning
Multi-level Residual Networks from Dynamical Systems View
Multi-Mention Learning for Reading Comprehension with Neural Cascades
Multiple Source Domain Adaptation with Adversarial Learning
Multi-Scale Dense Networks for Resource Efficient Image Classification
Multi-Task Learning for Document Ranking and Query Suggestion
Multi-View Data Generation Without View Supervision
N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning
NAM - Unsupervised Cross-Domain Image Mapping without Cycles or GANs
Natural Language Inference over Interaction Space
Negative eigenvalues of the Hessian in deep neural networks
NerveNet: Learning Structured Policy with Graph Neural Networks
Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks
Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples
Neural Language Modeling by Jointly Learning Syntax and Lexicon
Neural Map: Structured Memory for Deep Reinforcement Learning
Neural network parameter regression for lattice quantum chromodynamics simulations in nuclear and particle physics
Neural Program Search: Solving Programming Tasks from Description and Examples
Neural Sketch Learning for Conditional Program Generation
Neural Speed Reading via Skim-RNN
Neuron as an Agent
Noisy Networks For Exploration
Non-Autoregressive Neural Machine Translation
Nonlinear Acceleration of CNNs
No Spurious Local Minima in a Two Hidden Unit ReLU Network
Not-So-Random Features
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning
Online Learning Rate Adaptation with Hypergradient Descent
Online variance-reducing optimization
On the Convergence of Adam and Beyond
On the Discrimination-Generalization Tradeoff in GANs
On the Expressive Power of Overlapping Architectures of Deep Learning
On the importance of single directions for generalization
On the Information Bottleneck Theory of Deep Learning
On the insufficiency of existing momentum schemes for Stochastic Optimization
On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples
On the regularization of Wasserstein GANs
On the State of the Art of Evaluation in Neural Language Models
On Unifying Deep Generative Models
Overcoming Catastrophic Interference using Conceptor-Aided Backpropagation
Parallelizing Linear Recurrent Neural Nets Over Sequence Length
Parameter Space Noise for Exploration
Parametric Adversarial Divergences are Good Task Losses for Generative Modeling
Parametrized Hierarchical Procedures for Neural Programming
PDE-Net: Learning PDEs from Data
Pelee: A Real-Time Object Detection System on Mobile Devices
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
PixelNN: Example-based Image Synthesis
PixelSNAIL: An Improved Autoregressive Generative Model
Polar Transformer Networks
Policy Optimization by Genetic Distillation
Policy Optimization with Second-Order Advantage Information
PPP-Net: Platform-aware Progressive Search for Pareto-optimal Neural Architectures
Practical Hyperparameter Optimization
Predicting Embryo Morphokinetics in Videos with Late Fusion Nets & Dynamic Decoders
Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data
Predict Responsibly: Increasing Fairness by Learning to Defer
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control
Proximal Backpropagation
Quantitatively Evaluating GANs With Divergences Proposed for Training
Realistic Evaluation of Semi-Supervised Learning Algorithms
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes
Reconstructing evolutionary trajectories of mutations in cancer
Regret Minimization for Partially Observable Deep Reinforcement Learning
Regularization Neural Networks via Constrained Virtual Movement Field
Regularizing and Optimizing LSTM Language Models
Reinforcement Learning Algorithm Selection
Reinforcement Learning from Imperfect Demonstrations
Reinforcement Learning on Web Interfaces using Workflow-Guided Exploration
ReinforceWalk: Learning to Walk in Graph with Monte Carlo Tree Search
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions
Reproducibility, Reusability, and Robustness in Deep Reinforcement Learning
Residual Connections Encourage Iterative Inference
RESIDUAL LOSS PREDICTION: REINFORCEMENT LEARNING WITH NO INCREMENTAL FEEDBACK
Resilient Backpropagation (Rprop) for Batch-learning in TensorFlow
Rethinking Style and Content Disentanglement in Variational Autoencoders
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers
Reward Estimation for Variance Reduction in Deep Reinforcement Learning
Robustness of Classifiers to Universal Perturbations: A Geometric Perspective
Rotational Unit of Memory
Routing Networks: Adaptive Selection of Non-Linear Functions for Multi-Task Learning
Scalable Estimation via LSH Samplers (LSS)
Scalable Private Learning with PATE
SCAN: Learning Hierarchical Compositional Visual Concepts
Searching for Activation Functions
SEARNN: Training RNNs with global-local losses
Selecting the Best in GANs Family: a Post Selection Inference Framework
Self-ensembling for visual domain adaptation
Semantically Decomposing the Latent Spaces of Generative Adversarial Networks
Semantic Interpolation in Implicit Models
Semiparametric Reinforcement Learning
Semi-parametric topological memory for navigation
Semi-Supervised Few-Shot Learning with MAML
Semi-Supervised Learning With GANs: Revisiting Manifold Regularization
Sensitivity and Generalization in Neural Networks: an Empirical Study
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data
SGD on Random Mixtures: Private Machine Learning under Data Breach Threats
ShakeDrop regularization
Shifting Mean Activation Towards Zero with Bipolar Activation Functions
Simple and efficient architecture search for Convolutional Neural Networks
Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings
Simulating Action Dynamics with Neural Process Networks
Skip Connections Eliminate Singularities
Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks
SMASH: One-Shot Model Architecture Search through HyperNetworks
Smooth Loss Functions for Deep Top-k Classification
Sobolev GAN
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip
Spatially Parallel Convolutions
Spatially Transformed Adversarial Examples
Spectral Capsule Networks
SpectralNet: Spectral Clustering using Deep Neural Networks
Spectral Normalization for Generative Adversarial Networks
SpectralWords: Spectral Embeddings Approach to Word Similarity Task for Large Vocabularies
Spherical CNNs
Stabilizing Adversarial Nets with Prediction Methods
Stable and Effective Trainable Greedy Decoding for Sequence to Sequence Learning
Stable Distribution Alignment Using the Dual of the Adversarial Distance
Stacked Filters Stationary Flow For Hardware-Oriented Acceleration Of Deep Convolutional Neural Networks
Stochastic Activation Pruning for Robust Adversarial Defense
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
STOCHASTIC GRADIENT LANGEVIN DYNAMICS THAT EXPLOIT NEURAL NETWORK STRUCTURE
Stochastic Variational Video Prediction
SufiSent - Universal Sentence Representations Using Suffix Encodings
Syntax-Directed Variational Autoencoder for Structured Data
Synthesizing Audio with GANs
Synthesizing realistic neural population activity patterns using Generative Adversarial Networks
Synthetic and Natural Noise Both Break Neural Machine Translation
Systematic Weight Pruning of DNNs using Alternating Direction Method of Multipliers
TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning
Tempered Adversarial Networks
Temporal Difference Models: Model-Free Deep RL for Model-Based Control
Temporally Efficient Deep Learning with Spikes
THE EFFECTIVENESS OF A TWO-LAYER NEURAL NETWORK FOR RECOMMENDATIONS
The High-Dimensional Geometry of Binary Neural Networks
The Implicit Bias of Gradient Descent on Separable Data
The Kanerva Machine: A Generative Distributed Memory
The loss surface and expressivity of deep convolutional neural networks
The Mirage of Action-Dependent Baselines in Reinforcement Learning
The power of deeper networks for expressing natural functions
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
Thermometer Encoding: One Hot Way To Resist Adversarial Examples
The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings
Time-Dependent Representation for Neural Event Sequence Prediction
To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression
Towards better understanding of gradient-based attribution methods for Deep Neural Networks
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Image Understanding from Deep Compression Without Decoding
Towards Mixed-initiative generation of multi-channel sequential structure
Towards Neural Phrase-based Machine Translation
Towards Provable Control for Unknown Linear Dynamical Systems
Towards Reverse-Engineering Black-Box Neural Networks
Towards Specification-Directed Program Repair
Towards Synthesizing Complex Programs From Input-Output Examples
Towards Variational Generation of Small Graphs
Training and Inference with Integers in Deep Neural Networks
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
Training GANs with Optimism
TRAINING GENERATIVE ADVERSARIAL NETWORKS VIA PRIMAL-DUAL SUBGRADIENT METHODS: A LAGRANGIAN PERSPECTIVE ON GAN
Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks
Training wide residual networks for deployment using a single bit for each weight
TransNets for Review Generation
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
Tree-to-tree Neural Networks for Program Translation
TRUNCATED HORIZON POLICY SEARCH: COMBINING REINFORCEMENT LEARNING & IMITATION LEARNING
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control
Twin Networks: Matching the Future for Sequence Generation
Unbiased Online Recurrent Optimization
Uncertainty Estimation via Stochastic Batch Normalization
Understanding Deep Neural Networks with Rectified Linear Units
Understanding image motion with group representations
Understanding Short-Horizon Bias in Stochastic Meta-Optimization
Understanding the Loss Surface of Single-Layered Neural Networks for Binary Classification
Universal Agent for Disentangling Environments and Tasks
Universal Successor Representations for Transfer Reinforcement Learning
Unsupervised Cipher Cracking Using Discrete GANs
Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration
Unsupervised Machine Translation Using Monolingual Corpora Only
Unsupervised Neural Machine Translation
Unsupervised Representation Learning by Predicting Image Rotations
Variance-based Gradient Compression for Efficient Distributed Deep Learning
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
Variational Continual Learning
Variational image compression with a scale hyperprior
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
Variational Message Passing with Structured Inference Networks
Variational Network Quantization
Visual Learning With Unlabeled Video and Look-Around Policies
Viterbi-based Pruning for Sparse Matrix with Fixed and High Index Compression Ratio
VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop
Wasserstein Auto-Encoders
Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders
Wavelet Pooling for Convolutional Neural Networks
Weighted Geodesic Distance Following Fermat's Principle
Weightless: Lossy weight encoding for deep neural network compression
WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling
What Can Machine Learning Do? Workforce Implications
When is a Convolutional Filter Easy to Learn?
Winner's Curse? On Pace, Progress, and Empirical Rigor
Word translation without parallel data
WRPN: Wide Reduced-Precision Networks
Zero-Shot Visual Imitation