ICLR 2013 will be a 3-day event from May 2nd to May 4th 2013, co-located with AISTATS2013 in Scottsdale, Arizona. My Profile; My Event; Post Event; Searching By. - Chris. Note: It is generally recommended to submit your conference paper on or before the submission deadline. The conference includes invited talks as well as oral and poster presentations of refereed papers. PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees, FlowQA: Grasping Flow in History for Conversational Machine Comprehension, Identifying and Controlling Important Neurons in Neural Machine Translation, Generating High fidelity Images with subscale pixel Networks and Multidimensional Upscaling, Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks, Temporal Difference Variational Auto-Encoder, On Random Deep Weight-Tied Autoencoders: Exact Asymptotic Analysis, Phase Transitions, and Implications to Training, Learning a SAT Solver from Single-Bit Supervision, Analyzing Inverse Problems with Invertible Neural Networks, Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration, Information asymmetry in KL-regularized RL, BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning, Spectral Inference Networks: Unifying Deep and Spectral Learning, Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian Supervision, Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality, Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering, Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes, Improving MMD-GAN Training with Repulsive Loss Function, Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach, The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure, Learning concise representations for regression by evolving networks of trees, AD-VAT: An Asymmetric Dueling mechanism for learning Visual Active Tracking, There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average, Towards Understanding Regularization in Batch Normalization, Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator, Learning Mixed-Curvature Representations in Product Spaces, Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks, Visual Reasoning by Progressive Module Networks, Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet, Neural network gradient-based learning of black-box function interfaces, A new dog learns old tricks: RL finds classic optimization algorithms, Toward Understanding the Impact of Staleness in Distributed Machine Learning, Feed-forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers, Analysing Mathematical Reasoning Abilities of Neural Models, Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy, Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer, Unsupervised Control Through Non-Parametric Discriminative Rewards, Scalable Unbalanced Optimal Transport using Generative Adversarial Networks, Stable Opponent Shaping in Differentiable Games, The role of over-parametrization in generalization of neural networks, Discovery of Natural Language Concepts in Individual Units of CNNs, Knowledge Flow: Improve Upon Your Teachers, Meta-Learning Update Rules for Unsupervised Representation Learning, Large Scale Graph Learning From Smooth Signals, From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference, Learning Localized Generative Models for 3D Point Clouds via Graph Convolution, Meta-Learning For Stochastic Gradient MCMC, Predict then Propagate: Graph Neural Networks meet Personalized PageRank, Supervised Policy Update for Deep Reinforcement Learning, Generative predecessor models for sample-efficient imitation learning, Efficient Augmentation via Data Subsampling, ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, Unsupervised Discovery of Parts, Structure, and Dynamics, Representation Degeneration Problem in Training Natural Language Generation Models, Measuring Compositionality in Representation Learning, Universal Successor Features Approximators, Three Mechanisms of Weight Decay Regularization, Small nonlinearities in activation functions create bad local minima in neural networks, MisGAN: Learning from Incomplete Data with Generative Adversarial Networks, Transfer Learning for Sequences via Learning to Collocate, Adversarial Domain Adaptation for Stable Brain-Machine Interfaces, Contingency-Aware Exploration in Reinforcement Learning, Eidetic 3D LSTM: A Model for Video Prediction and Beyond, From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following, Lagging Inference Networks and Posterior Collapse in Variational Autoencoders, Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset, Dimensionality Reduction for Representing the Knowledge of Probabilistic Models, KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks, Generalized Tensor Models for Recurrent Neural Networks, Approximability of Discriminators Implies Diversity in GANs, Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability, Learning to Infer and Execute 3D Shape Programs, Sample Efficient Imitation Learning for Continuous Control, Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks, Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference, Relational Forward Models for Multi-Agent Learning, Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network, Texttovec: Deep Contextualized Neural autoregressive Topic Models of Language with Distributed Compositional Prior, Context-adaptive Entropy Model for End-to-end Optimized Image Compression, RNNs implicitly implement tensor-product representations, Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer, LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators, AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks, Predicting the Generalization Gap in Deep Networks with Margin Distributions, A Direct Approach to Robust Deep Learning Using Adversarial Networks, Music Transformer: Generating Music with Long-Term Structure, Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure, Deep, Skinny Neural Networks are not Universal Approximators, Human-level Protein Localization with Convolutional Neural Networks, Information-Directed Exploration for Deep Reinforcement Learning, Learning Factorized Multimodal Representations, Learning Multi-Level Hierarchies with Hindsight, Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency, Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization, The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks, Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks, Deep reinforcement learning with relational inductive biases, Learnable Embedding Space for Efficient Neural Architecture Compression, A Statistical Approach to Assessing Neural Network Robustness, Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks, Aggregated Momentum: Stability Through Passive Damping, Unsupervised Learning of the Set of Local Maxima, Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids, Learning to Learn with Conditional Class Dependencies, Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies, Synthetic Datasets for Neural Program Synthesis, Smoothing the Geometry of Probabilistic Box Embeddings, FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models, GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding, Graph HyperNetworks for Neural Architecture Search, NOODL: Provable Online Dictionary Learning and Sparse Coding, Learning Embeddings into Entropic Wasserstein Spaces, Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods, Generating Multiple Objects at Spatially Distinct Locations, Boosting Robustness Certification of Neural Networks, G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space, Towards GAN Benchmarks Which Require Generalization, Multi-class classification without multi-class labels, Fluctuation-dissipation relations for stochastic gradient descent, Deterministic Variational Inference for Robust Bayesian Neural Networks, Function Space Particle Optimization for Bayesian Neural Networks, SOM-VAE: Interpretable Discrete Representation Learning on Time Series, Learning Factorized Representations for Open-Set Domain Adaptation, Time-Agnostic Prediction: Predicting Predictable Video Frames, Deep Anomaly Detection with Outlier Exposure, Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach, Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors, Distribution-Interpolation Trade off in Generative Models, Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search, Deep Frank-Wolfe For Neural Network Optimization, Phase-Aware Speech Enhancement with Deep Complex U-Net, Deep learning generalizes because the parameter-function map is biased towards simple functions, SGD Converges to Global Minimum in Deep Learning via Star-convex Path, Towards the first adversarially robust neural network model on MNIST, On Self Modulation for Generative Adversarial Networks, Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures, Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach, Learning to Remember More with Less Memorization, Quasi-hyperbolic momentum and Adam for deep learning, Preferences Implicit in the State of the World, Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces, Learning to Understand Goal Specifications by Modelling Reward, Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition, A Max-Affine Spline Perspective of Recurrent Neural Networks, Revealing interpretable object representations from human behavior, Marginalized Average Attentional Network for Weakly-Supervised Learning, Harmonic Unpaired Image-to-image Translation, Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees, Gradient Descent Provably Optimizes Over-parameterized Neural Networks, AutoLoss: Learning Discrete Schedule for Alternate Optimization, Integer Networks for Data Compression with Latent-Variable Models, Learning deep representations by mutual information estimation and maximization, LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos, Visceral Machines: Risk-Aversion in Reinforcement Learning with Intrinsic Physiological Rewards, Improving Generalization and Stability of Generative Adversarial Networks, Imposing Category Trees Onto Word-Embeddings Using A Geometric Construction, On Computation and Generalization of Generative Adversarial Networks under Spectrum Control, Dynamic Channel Pruning: Feature Boosting and Suppression, Evaluating Robustness of Neural Networks with Mixed Integer Programming, An analytic theory of generalization dynamics and transfer learning in deep linear networks, Efficiently testing local optimality and escaping saddles for ReLU networks, Cost-Sensitive Robustness against Adversarial Examples, Learning sparse relational transition models, Information Theoretic lower bounds on negative log likelihood, Robust Conditional Generative Adversarial Networks, On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length, Interpolation-Prediction Networks for Irregularly Sampled Time Series, ADef: an Iterative Algorithm to Construct Adversarial Deformations, Towards Robust, Locally Linear Deep Networks, Poincare Glove: Hyperbolic Word Embeddings, PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks, Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder, DHER: Hindsight Experience Replay for Dynamic Goals, Diversity-Sensitive Conditional Generative Adversarial Networks, Initialized Equilibrium Propagation for Backprop-Free Training, The Unusual Effectiveness of Averaging in GAN Training, Caveats for information bottleneck in deterministic scenarios, Characterizing Audio Adversarial Examples Using Temporal Dependency, Adaptive Posterior Learning: few-shot learning with a surprise-based memory module, RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks, Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking, Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience, Verification of Non-Linear Specifications for Neural Networks, Neural TTS Stylization with Adversarial and Collaborative Games, Learning to Describe Scenes with Programs, Policy Transfer with Strategy Optimization, NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning, code2seq: Generating Sequences from Structured Representations of Code, Kernel Change-point Detection with Auxiliary Deep Generative Models, Neural Probabilistic Motor Primitives for Humanoid Control, Differentiable Learning-to-Normalize via Switchable Normalization, Soft Q-Learning with Mutual-Information Regularization, On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization, INVASE: Instance-wise Variable Selection using Neural Networks, Adaptive Gradient Methods with Dynamic Bound of Learning Rate, Preconditioner on Matrix Lie Group for SGD, Pay Less Attention with Lightweight and Dynamic Convolutions, Critical Learning Periods in Deep Networks, Learning Exploration Policies for Navigation, Dynamic Sparse Graph for Efficient Deep Learning, Meta-learning with differentiable closed-form solvers, Deep Learning 3D Shapes Using Alt-az Anisotropic 2-Sphere Convolution, A rotation-equivariant convolutional neural network model of primary visual cortex, SPIGAN: Privileged Adversarial Learning from Simulation, Disjoint Mapping Network for Cross-modal Matching of Voices and Faces, A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs, Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks, Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams, Hierarchical Visuomotor Control of Humanoids, Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension, Wizard of Wikipedia: Knowledge-Powered Conversational Agents, Learning Actionable Representations with Goal Conditioned Policies, Adaptive Input Representations for Neural Language Modeling, GANSynth: Adversarial Neural Audio Synthesis, Modeling the Long Term Future in Model-Based Reinforcement Learning, Adaptive Estimators Show Information Compression in Deep Neural Networks, Large Scale GAN Training for High Fidelity Natural Image Synthesis, Learning Robust Representations by Projecting Superficial Statistics Out, Learning Recurrent Binary/Ternary Weights, The relativistic discriminator: a key element missing from standard GAN, The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision, Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation, Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information, Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors, Bayesian Policy Optimization for Model Uncertainty, Learning Representations of Sets through Optimized Permutations, Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization, Global-to-local Memory Pointer Networks for Task-Oriented Dialogue, Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering, M^3RL: Mind-aware Multi-agent Management Reinforcement Learning, Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology, Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs, InfoBot: Transfer and Exploration via the Information Bottleneck, Learning a Meta-Solver for Syntax-Guided Program Synthesis, What do you learn from context? 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