Below are the Workshop 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 Workshop Track.

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

W1: Extrapolation and learning equations
W2: Effectiveness of Transfer Learning in EHR data
W3: Intelligent synapses for multi-task and transfer learning
W4: Unsupervised and Efficient Neural Graph Model with Distributed Representations
W5: Accelerating SGD for Distributed Deep-Learning Using an Approximted Hessian Matrix
W6: Accelerating Eulerian Fluid Simulation With Convolutional Networks
W7: Forced to Learn: Discovering Disentangled Representations Without Exhaustive Labels
W8: Dataset Augmentation in Feature Space
W9: Learning Algorithms for Active Learning
W10: Reinterpreting Importance-Weighted Autoencoders
W11: Robustness to Adversarial Examples through an Ensemble of Specialists
W12: (empty)
W13: On Hyperparameter Optimization in Learning Systems
W14: Recurrent Normalization Propagation
W15: Joint Training of Ratings and Reviews with Recurrent Recommender Networks
W16: Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses
W17: Joint Embeddings of Scene Graphs and Images
W18: Unseen Style Transfer Based on a Conditional Fast Style Transfer Network

W1: Audio Super-Resolution using Neural Networks
W2: Semantic embeddings for program behaviour patterns
W3: De novo drug design with deep generative models : an empirical study
W4: Memory Matching Networks for Genomic Sequence Classification
W5: Char2Wav: End-to-End Speech Synthesis
W6: Fast Chirplet Transform Injects Priors in Deep Learning of Animal Calls and Speech
W7: Weight-averaged consistency targets improve semi-supervised deep learning results
W8: Particle Value Functions
W9: Out-of-class novelty generation: an experimental foundation
W10: Performance guarantees for transferring representations
W11: Generative Adversarial Learning of Markov Chains
W12: Short and Deep: Sketching and Neural Networks
W13: Understanding intermediate layers using linear classifier probes
W14: Symmetry-Breaking Convergence Analysis of Certain Two-layered Neural Networks with ReLU nonlinearity
W15: Neural Combinatorial Optimization with Reinforcement Learning
W16: Tactics of Adversarial Attacks on Deep Reinforcement Learning Agents
W17: Adversarial Discriminative Domain Adaptation (workshop extended abstract)
W18: Efficient Sparse-Winograd Convolutional Neural Networks
W19: Neural Expectation Maximization

W1: Programming With a Differentiable Forth Interpreter
W2: Unsupervised Feature Learning for Audio Analysis
W3: Neural Functional Programming
W4: A Smooth Optimisation Perspective on Training Feedforward Neural Networks
W5: Synthetic Gradient Methods with Virtual Forward-Backward Networks
W6: Explaining the Learning Dynamics of Direct Feedback Alignment
W7: Training a Subsampling Mechanism in Expectation
W8: Deep Kernel Machines via the Kernel Reparametrization Trick
W9: Encoding and Decoding Representations with Sum- and Max-Product Networks
W10: Embracing Data Abundance
W11: Variational Intrinsic Control
W12: Fast Adaptation in Generative Models with Generative Matching Networks
W13: Efficient variational Bayesian neural network ensembles for outlier detection
W14: Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols
W15: Adaptive Feature Abstraction for Translating Video to Language
W16: Delving into adversarial attacks on deep policies
W17: Tuning Recurrent Neural Networks with Reinforcement Learning
W18: DeepMask: Masking DNN Models for robustness against adversarial samples
W19: Restricted Boltzmann Machines provide an accurate metric for retinal responses to visual stimuli

W1: Lifelong Perceptual Programming By Example
W2: Neu0
W3: Dance Dance Convolution
W4: Bit-Pragmatic Deep Neural Network Computing
W5: On Improving the Numerical Stability of Winograd Convolutions
W6: Fast Generation for Convolutional Autoregressive Models
W7: THE PREIMAGE OF RECTIFIER NETWORK ACTIVITIES
W8: Training Triplet Networks with GAN
W9: On Robust Concepts and Small Neural Nets
W10: Pl@ntNet app in the era of deep learning
W11: Exponential Machines
W12: Online Multi-Task Learning Using Biased Sampling
W13: Online Structure Learning for Sum-Product Networks with Gaussian Leaves
W14: A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples
W15: Compositional Kernel Machines
W16: Loss is its own Reward: Self-Supervision for Reinforcement Learning
W17: REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
W18: Precise Recovery of Latent Vectors from Generative Adversarial Networks
W19: Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

W1: NEUROGENESIS-INSPIRED DICTIONARY LEARNING: ONLINE MODEL ADAPTION IN A CHANGING WORLD
W2: The High-Dimensional Geometry of Binary Neural Networks
W3: Discovering objects and their relations from entangled scene representations
W4: A Differentiable Physics Engine for Deep Learning in Robotics
W5: Automated Generation of Multilingual Clusters for the Evaluation of Distributed Representations
W6: Development of JavaScript-based deep learning platform and application to distributed training
W7: Factorization tricks for LSTM networks
W8: Shake-Shake regularization of 3-branch residual networks
W9: Trace Norm Regularised Deep Multi-Task Learning
W10: Deep Learning with Sets and Point Clouds
W11: Deep Nets Don't Learn via Memorization
W12: Multiplicative LSTM for sequence modelling
W13: Learning to Discover Sparse Graphical Models
W14: Revisiting Batch Normalization For Practical Domain Adaptation
W15: Early Methods for Detecting Adversarial Images and a Colorful Saliency Map
W16: Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data
W17: Coupling Distributed and Symbolic Execution for Natural Language Queries
W18: Adversarial Examples for Semantic Image Segmentation
W19: RenderGAN: Generating Realistic Labeled Data

W1: Song From PI: A Musically Plausible Network for Pop Music Generation
W2: Charged Point Normalization: An Efficient Solution to the Saddle Point Problem
W3: Towards “AlphaChem”: Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies
W4: CommAI: Evaluating the first steps towards a useful general AI
W5: Joint Multimodal Learning with Deep Generative Models
W6: Transferring Knowledge to Smaller Network with Class-Distance Loss
W7: Regularizing Neural Networks by Penalizing Confident Output Distributions
W8: Adversarial Attacks on Neural Network Policies
W9: Generalizable Features From Unsupervised Learning
W10: Compact Embedding of Binary-coded Inputs and Outputs using Bloom Filters
W11: Semi-supervised deep learning by metric embedding
W12: Changing Model Behavior at Test-time Using Reinforcement Learning
W13: Variational Reference Priors
W14: Gated Multimodal Units for Information Fusion
W15: Playing SNES in the Retro Learning Environment
W16: Unsupervised Perceptual Rewards for Imitation Learning
W17: Perception Updating Networks: On architectural constraints for interpretable video generative models
W18: Adversarial examples in the physical world