ICLR 2017

Conference Poster Sessions

Below are the Conference Track papers presented at each of the poster sessions (on Monday, Tuesday or Wednesday, in the morning or evening). To find a paper, look for the poster with the corresponding number in the area dedicated to the Conference Track.

Note to the Presenters

Each poster panel is 2 meters large and 1 meter tall.
If needed, tape will be provided to fix your poster.

Monday Morning (April 24th, 10:30am to 12:30pm)

C1: Making Neural Programming Architectures Generalize via Recursion
C2: Learning Graphical State Transitions
C3: Distributed Second-Order Optimization using Kronecker-Factored Approximations
C4: Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes
C5: Neural Program Lattices
C6: Diet Networks: Thin Parameters for Fat Genomics
C7: Unsupervised Cross-Domain Image Generation
C8: Towards Principled Methods for Training Generative Adversarial Networks
C9: Recurrent Mixture Density Network for Spatiotemporal Visual Attention
C10: Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
C11: Pruning Filters for Efficient ConvNets
C12: Stick-Breaking Variational Autoencoders
C13: Identity Matters in Deep Learning
C14: On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
C15: Recurrent Hidden Semi-Markov Model
C16: Nonparametric Neural Networks
C17: Learning to Generate Samples from Noise through Infusion Training
C18: An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax
C19: Highway and Residual Networks learn Unrolled Iterative Estimation
C20: Soft Weight-Sharing for Neural Network Compression
C21: Snapshot Ensembles: Train 1, Get M for Free
C22: Towards a Neural Statistician
C23: Learning Curve Prediction with Bayesian Neural Networks
C24: Learning End-to-End Goal-Oriented Dialog
C25: Multi-Agent Cooperation and the Emergence of (Natural) Language
C26: Efficient Vector Representation for Documents through Corruption
C27: Improving Neural Language Models with a Continuous Cache
C28: Program Synthesis for Character Level Language Modeling
C29: Tracking the World State with Recurrent Entity Networks
C30: Reinforcement Learning with Unsupervised Auxiliary Tasks
C31: Neural Architecture Search with Reinforcement Learning
C32: Sample Efficient Actor-Critic with Experience Replay
C33: Learning to Act by Predicting the Future

Monday Afternoon (April 24th, 4:30pm to 6:30pm)

C1: Neuro-Symbolic Program Synthesis
C2: Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
C3: Trained Ternary Quantization
C4: DSD: Dense-Sparse-Dense Training for Deep Neural Networks
C5: A Compositional Object-Based Approach to Learning Physical Dynamics
C6: Multilayer Recurrent Network Models of Primate Retinal Ganglion Cells
C7: Improving Generative Adversarial Networks with Denoising Feature Matching
C8: Transfer of View-manifold Learning to Similarity Perception of Novel Objects
C9: What does it take to generate natural textures?
C10: Emergence of foveal image sampling from learning to attend in visual scenes
C11: PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications
C12: Learning to Optimize
C13: Do Deep Convolutional Nets Really Need to be Deep and Convolutional?
C14: Optimal Binary Autoencoding with Pairwise Correlations
C15: On the Quantitative Analysis of Decoder-Based Generative Models
C16: Adversarial machine learning at scale
C17: Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
C18: Capacity and Learnability in Recurrent Neural Networks
C19: Deep Learning with Dynamic Computation Graphs
C20: Exploring Sparsity in Recurrent Neural Networks
C21: Structured Attention Networks
C22: Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning
C23: Variational Lossy Autoencoder
C24: Learning to Query, Reason, and Answer Questions On Ambiguous Texts
C25: Deep Biaffine Attention for Neural Dependency Parsing
C26: A Compare-Aggregate Model for Matching Text Sequences
C27: Data Noising as Smoothing in Neural Network Language Models
C28: Neural Variational Inference For Topic Models
C29: Bidirectional Attention Flow for Machine Comprehension
C30: Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
C31: Stochastic Neural Networks for Hierarchical Reinforcement Learning
C32: Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning
C33: Third Person Imitation Learning

Tuesday Morning (April 25th, 10:30am to 12:30pm)

C1: DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning
C3: Deep Probabilistic Programming
C4: Lie-Access Neural Turing Machines
C5: Learning Features of Music From Scratch
C6: Mode Regularized Generative Adversarial Networks
C7: End-to-end Optimized Image Compression
C8: Variational Recurrent Adversarial Deep Domain Adaptation
C9: Steerable CNNs
C10: Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
C11: PixelVAE: A Latent Variable Model for Natural Images
C12: A recurrent neural network without chaos
C13: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
C14: Tree-structured decoding with doubly-recurrent neural networks
C15: Introspection:Accelerating Neural Network Training By Learning Weight Evolution
C16: Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization
C17: Quasi-Recurrent Neural Networks
C18: Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain
C19: A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
C20: Trusting SVM for Piecewise Linear CNNs
C21: Maximum Entropy Flow Networks
C22: The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
C23: Unrolled Generative Adversarial Networks
C24: A Simple but Tough-to-Beat Baseline for Sentence Embeddings
C25: Query-Reduction Networks for Question Answering
C26: Machine Comprehension Using Match-LSTM and Answer Pointer
C27: Words or Characters? Fine-grained Gating for Reading Comprehension
C28: Dynamic Coattention Networks For Question Answering
C29: Multi-view Recurrent Neural Acoustic Word Embeddings
C30: Episodic Exploration for Deep Deterministic Policies for StarCraft Micromanagement
C31: Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning
C32: Generalizing Skills with Semi-Supervised Reinforcement Learning
C33: Improving Policy Gradient by Exploring Under-appreciated Rewards

Tuesday Afternoon (April 25th, 2:00pm to 4:00pm)

C1: Sigma Delta Quantized Networks
C2: Paleo: A Performance Model for Deep Neural Networks
C3: DeepCoder: Learning to Write Programs
C4: Topology and Geometry of Deep Rectified Network Optimization Landscapes
C5: Incremental Network Quantization: Towards Lossless CNNs with Low-precision Weights
C6: Learning to Perform Physics Experiments via Deep Reinforcement Learning
C7: Decomposing Motion and Content for Natural Video Sequence Prediction
C8: Calibrating Energy-based Generative Adversarial Networks
C9: Pruning Convolutional Neural Networks for Resource Efficient Inference
C10: Incorporating long-range consistency in CNN-based texture generation
C11: Lossy Image Compression with Compressive Autoencoders
C12: LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
C13: Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
C14: Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
C15: Mollifying Networks
C16: beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
C17: Categorical Reparameterization with Gumbel-Softmax
C18: Online Bayesian Transfer Learning for Sequential Data Modeling
C19: Latent Sequence Decompositions
C20: Density estimation using Real NVP
C21: Recurrent Batch Normalization
C22: SGDR: Stochastic Gradient Descent with Restarts
C23: Variable Computation in Recurrent Neural Networks
C24: Deep Variational Information Bottleneck
C25: SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
C26: TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency
C27: Frustratingly Short Attention Spans in Neural Language Modeling
C28: Offline Bilingual Word Vectors, Orthogonal Transformations and the Inverted Softmax
C30: Designing Neural Network Architectures using Reinforcement Learning
C31: Metacontrol for Adaptive Imagination-Based Optimization
C32: Recurrent Environment Simulators
C33: EPOpt: Learning Robust Neural Network Policies Using Model Ensembles

Wednesday Morning (April 26th, 10:30am to 12:30pm)

C1: Deep Multi-task Representation Learning: A Tensor Factorisation Approach
C2: Training deep neural-networks using a noise adaptation layer
C3: Delving into Transferable Adversarial Examples and Black-box Attacks
C4: Towards the Limit of Network Quantization
C5: Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music
C6: Learning to superoptimize programs
C7: Regularizing CNNs with Locally Constrained Decorrelations
C8: Generative Multi-Adversarial Networks
C9: Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
C10: FractalNet: Ultra-Deep Neural Networks without Residuals
C11: Faster CNNs with Direct Sparse Convolutions and Guided Pruning
C13: The Neural Noisy Channel
C14: Automatic Rule Extraction from Long Short Term Memory Networks
C15: Adversarially Learned Inference
C16: Deep Information Propagation
C17: Revisiting Classifier Two-Sample Tests
C18: Loss-aware Binarization of Deep Networks
C19: Energy-based Generative Adversarial Networks
C20: Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning
C21: Temporal Ensembling for Semi-Supervised Learning
C22: On Detecting Adversarial Perturbations
C23: Understanding deep learning requires rethinking generalization
C24: Adversarial Feature Learning
C25: Learning through Dialogue Interactions
C26: Learning to Compose Words into Sentences with Reinforcement Learning
C27: Batch Policy Gradient Methods for Improving Neural Conversation Models
C28: Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling
C29: Geometry of Polysemy
C30: PGQ: Combining policy gradient and Q-learning
C31: Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU
C32: Learning to Navigate in Complex Environments
C33: Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks

Wednesday Afternoon (April 26th, 4:30pm to 6:30pm)

C1: Learning recurrent representations for hierarchical behavior modeling
C2: Predicting Medications from Diagnostic Codes with Recurrent Neural Networks
C3: Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks
C4: HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving
C5: Learning Invariant Representations Of Planar Curves
C6: Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
C7: Amortised MAP Inference for Image Super-resolution
C8: Inductive Bias of Deep Convolutional Networks through Pooling Geometry
C9: Neural Photo Editing with Introspective Adversarial Networks
C10: A Learned Representation For Artistic Style
C11: Learning to Remember Rare Events
C12: Optimization as a Model for Few-Shot Learning
C13: Support Regularized Sparse Coding and Its Fast Encoder
C14: Discrete Variational Autoencoders
C15: Training Compressed Fully-Connected Networks with a Density-Diversity Penalty
C16: Efficient Representation of Low-Dimensional Manifolds using Deep Networks
C17: Semi-Supervised Classification with Graph Convolutional Networks
C18: Understanding Neural Sparse Coding with Matrix Factorization
C19: Tighter bounds lead to improved classifiers
C20: Why Deep Neural Networks for Function Approximation?
C21: Hierarchical Multiscale Recurrent Neural Networks
C22: Dropout with Expectation-linear Regularization
C23: HyperNetworks
C24: Hadamard Product for Low-rank Bilinear Pooling
C25: Adversarial Training Methods for Semi-Supervised Text Classification
C26: Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks
C27: Pointer Sentinel Mixture Models
C28: Reasoning with Memory Augmented Neural Networks for Language Comprehension
C29: Dialogue Learning With Human-in-the-Loop
C30: Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
C31: Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening
C32: Learning Visual Servoing with Deep Features and Trust Region Fitted Q-Iteration
C33: An Actor-Critic Algorithm for Sequence Prediction