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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | 0 | 0 | null | main | 0 | null | null | null | DEEPCAST : UNIVERSAL TIME-SERIES FORECASTER | null | null | 0 | 0 | Withdraw | null | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | conversation model;multimodal embedding;attention mechanism;natural language processing;encoder-decoder model | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 3.333333 | 3;3;4 | null | null | Associative Conversation Model: Generating Visual Information from Textual Information | null | null | 0 | 4.666667 | Reject | 4;5;5 | null |
null | The Hebrew University of Jerusalem | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | recurrent neural networks;deep networks;correlations;long term memory;tensor networks;tensor analysis | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 6 | 5;6;7 | null | null | Benefits of Depth for Long-Term Memory of Recurrent Networks | null | null | 0 | 2.666667 | Workshop | 2;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Active Learning;Deep Reinforcement Learning | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 6.333333 | 6;6;7 | null | null | Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning | null | null | 0 | 3.666667 | Reject | 3;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep learning;tensor product representation;LSTM;image captioning | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 4.333333 | 4;4;5 | null | null | A Neural-Symbolic Approach to Natural Language Tasks | null | null | 0 | 4.333333 | Reject | 4;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | generative models;one-shot learning;metalearning;pixelcnn;hierarchical bayesian;omniglot | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 6 | 5;6;7 | null | null | The Variational Homoencoder: Learning to Infer High-Capacity Generative Models from Few Examples | null | null | 0 | 4 | Reject | 5;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | fast weights;RNN;associative retrieval;time-varying variables | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 4 | 3;4;5 | null | null | GATED FAST WEIGHTS FOR ASSOCIATIVE RETRIEVAL | null | null | 0 | 4.333333 | Reject | 5;4;4 | null |
null | College of Information Sciences and Technology, The Pennsylvania State University; Adobe Research | 2018 | 0 | null | null | 0 | null | null | null | null | null | Jianbo Ye, Xin Lu, Zhe Lin, James Z Wang | https://iclr.cc/virtual/2018/poster/315 | model pruning;batch normalization;convolutional neural network;ISTA | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 6 | 5;6;7 | null | null | Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers | null | null | 0 | 4.333333 | Poster | 5;5;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Generalization;Neural Networks;Fourier Analysis | null | 0 | null | null | iclr | -1 | 0 | null | main | 5.333333 | 4;6;6 | null | null | A Spectral Approach to Generalization and Optimization in Neural Networks | null | null | 0 | 3.333333 | Reject | 4;3;3 | null |
null | Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein | https://iclr.cc/virtual/2018/poster/65 | generalization;complexity;experimental study;linear regions;Jacobian | null | 0 | null | null | iclr | -0.240192 | 0 | null | main | 5.666667 | 4;5;8 | null | null | Sensitivity and Generalization in Neural Networks: an Empirical Study | null | null | 0 | 4 | Poster | 5;3;4 | null |
null | Cornell University, Ithaca, NY 14850, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Ben Athiwaratkun, Andrew Wilson | https://iclr.cc/virtual/2018/poster/7 | embeddings;word embeddings;probabilistic embeddings;hierarchical representation;probabilistic representation;order embeddings;wordnet;hyperlex | null | 0 | null | null | iclr | 1 | 0 | null | main | 6 | 4;6;8 | null | null | Hierarchical Density Order Embeddings | null | null | 0 | 4 | Poster | 3;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | objects;unsupervised;reinforcement learning;atari | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 3.666667 | 3;4;4 | null | null | Learning objects from pixels | null | null | 0 | 3.666667 | Reject | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep Learning;Computer Vision;Approximation | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 4;5;6 | null | null | Heterogeneous Bitwidth Binarization in Convolutional Neural Networks | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Adversarial Examples;Detection;Saliency;Model Interpretation | null | 0 | null | null | iclr | -1 | 0 | null | main | 3.75 | 3;4;4;4 | null | null | DETECTING ADVERSARIAL PERTURBATIONS WITH SALIENCY | null | null | 0 | 4.25 | Withdraw | 5;4;4;4 | null |
null | Department of Precision Instrument, Center for Brain Inspired Computing Research, Beijing Innovation Center for Future Chip, Tsinghua University; Department of Automation, Tsinghua University | 2018 | 0 | null | null | 0 | null | null | null | null | null | Shuang Wu, Guoqi Li, Feng Chen, Luping Shi | https://iclr.cc/virtual/2018/poster/330 | quantization;training;bitwidth;ternary weights | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 7.333333 | 7;7;8 | null | null | Training and Inference with Integers in Deep Neural Networks | null | null | 0 | 3.666667 | Oral | 3;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | meshes;convolutions;faces;autoencoder | null | 0 | null | null | iclr | -1 | 0 | null | main | 4 | 2;4;6 | null | null | Convolutional Mesh Autoencoders for 3D Face Representation | null | null | 0 | 4 | Reject | 5;4;3 | null |
null | DeepMind, London, UK | 2018 | 0 | null | null | 0 | null | null | null | null | null | Angeliki Lazaridou, Karl M Hermann, Karl Tuyls, Stephen Clark | https://iclr.cc/virtual/2018/poster/138 | disentanglement;communication;emergent language;compositionality;multi-agent | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 7 | 5;7;9 | null | null | Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input | null | null | 0 | 4.333333 | Oral | 4;4;5 | null |
null | Under Review at ICLR 2018 | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Prediction Under Uncertainty with Error Encoding Networks | null | null | 0 | 3 | Reject | 4;2;3 | null |
null | Boston University; The University of Tokyo, RIKEN; The University of Tokyo | 2018 | 0 | null | null | 0 | null | null | null | null | null | Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, Kate Saenko | https://iclr.cc/virtual/2018/poster/137 | domain adaptation;computer vision;generative models | null | 0 | null | null | iclr | 0.327327 | 0 | null | main | 6.666667 | 5;7;8 | null | null | Adversarial Dropout Regularization | null | null | 0 | 4 | Poster | 4;3;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | RNNs;time series forecasting;nonlinear dynamics;tensor-train | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 4;5;6 | null | null | Long-term Forecasting using Tensor-Train RNNs | null | null | 0 | 3.666667 | Reject | 4;3;4 | null |
null | Carnegie Mellon University; Google Brain; Georgia Tech | 2018 | 0 | null | null | 0 | null | null | null | null | null | Krzysztof Choromanski, Carlton Downey, Byron Boots | https://iclr.cc/virtual/2018/poster/109 | recurrent neural networks;orthogonal random features;predictive state representations | null | 0 | null | null | iclr | -0.576557 | 0 | null | main | 6.333333 | 4;7;8 | null | null | Initialization matters: Orthogonal Predictive State Recurrent Neural Networks | null | null | 0 | 3.666667 | Poster | 5;2;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Parsimonious Deep Feed-forward Networks;structure learning;classification;overfitting;fewer parameters;high interpretability | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Learning Parsimonious Deep Feed-forward Networks | null | null | 0 | 3 | Reject | 2;2;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | image captioning;representation learning;interpretability;rnn;multimodal;vision to language | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 4;4;4 | null | null | What is image captioning made of? | null | null | 0 | 4.666667 | Reject | 5;4;5 | null |
null | University of Maryland; Microsoft Research NYC; University of Maryland & Microsoft Research NYC | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Reinforcement Learning;Structured Prediction;Contextual Bandits;Learning Reduction | null | 0 | null | null | iclr | -0.188982 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback | null | null | 0 | 3.666667 | Poster | 4;2;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | -1 | 0 | null | main | 6 | 5;6;7 | null | null | Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum | null | null | 0 | 4 | Reject | 5;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep reinforcement learning;Computer Vision;Multi-modal fusion;Language Grounding | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 4.333333 | 4;4;5 | null | null | Learning to navigate by distilling visual information and natural language instructions | null | null | 0 | 4 | Reject | 5;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | reading comprehension;question answering;CNN;ConvNet;Inference | null | 0 | null | null | iclr | -0.944911 | 0 | null | main | 5.333333 | 4;5;7 | null | null | FAST READING COMPREHENSION WITH CONVNETS | null | null | 0 | 3.666667 | Reject | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | optimization;generalization;Adam;SGD | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Normalized Direction-preserving Adam | null | null | 0 | 4.333333 | Reject | 4;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | interpretability;regularization;deep learning;graphical models;model diagnostics;survival analysis | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 6;6;6 | null | null | Contextual Explanation Networks | null | null | 0 | 3.333333 | Reject | 2;5;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | reinforcement learning;imitation learning;robotics;visuomotor skills | null | 0 | null | null | iclr | 1 | 0 | null | main | 4.666667 | 4;4;6 | null | null | Reinforcement and Imitation Learning for Diverse Visuomotor Skills | null | null | 0 | 4.333333 | Reject | 4;4;5 | null |
null | Google Brain, Toronto, Canada | 2018 | 0 | null | null | 0 | null | null | null | null | null | Geoffrey E Hinton, Sara Sabour, Nicholas Frosst | https://iclr.cc/virtual/2018/poster/87 | Computer Vision;Deep Learning;Dynamic routing | null | 0 | null | null | iclr | 0.944911 | 0 | null | main | 5.666667 | 4;6;7 | null | null | Matrix capsules with EM routing | null | null | 0 | 2.666667 | Poster | 2;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep Compression;Deep Learning;Parent-Teacher Networks | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Deep Net Triage: Assessing The Criticality of Network Layers by Structural Compression | null | null | 0 | 3.666667 | Withdraw | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | 0 | 0 | null | main | 0 | null | null | null | title | null | null | 0 | 0 | Withdraw | null | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | localist;pdp;neural network;representation;psychology;cognition | null | 0 | null | null | iclr | 0 | 0 | null | main | 3.666667 | 3;3;5 | null | null | When and where do feed-forward neural networks learn localist representations? | null | null | 0 | 4 | Reject | 5;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | conversation model;seq2seq;self-play;reinforcement learning | null | 0 | null | null | iclr | -0.755929 | 0 | null | main | 4.333333 | 3;4;6 | null | null | A Goal-oriented Neural Conversation Model by Self-Play | null | null | 0 | 3.333333 | Reject | 4;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | GANs;first order dynamics;convergence;mode collapse | null | 0 | null | null | iclr | -0.755929 | 0 | null | main | 5.333333 | 4;5;7 | null | null | On the limitations of first order approximation in GAN dynamics | null | null | 0 | 3.333333 | Workshop | 4;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | -0.327327 | 0 | null | main | 4.666667 | 3;5;6 | null | null | A comparison of second-order methods for deep convolutional neural networks | null | null | 0 | 4 | Reject | 4;5;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.666667 | 5;5;7 | null | null | Byte-Level Recursive Convolutional Auto-Encoder for Text | null | null | 0 | 4 | Reject | 3;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Spectral Method;Multi-label Learning;Tensor Factorisation | null | 0 | null | null | iclr | 1 | 0 | null | main | 3.666667 | 3;4;4 | null | null | Multi-label Learning for Large Text Corpora using Latent Variable Model with Provable Gurantees | null | null | 0 | 4.666667 | Reject | 4;5;5 | null |
null | Weizmann Institute of Science, Rehovot, Israel; Yale University, New Haven, CT, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Uri Shaham, Kelly Stanton, Henry Li, Ronen Basri, Boaz Nadler, Yuval Kluger | https://iclr.cc/virtual/2018/poster/290 | unsupervised learning;spectral clustering;siamese networks | null | 0 | null | null | iclr | 0.327327 | 0 | null | main | 5.666667 | 4;6;7 | null | null | SpectralNet: Spectral Clustering using Deep Neural Networks | https://github.com/kstant0725/SpectralNet | null | 0 | 4 | Poster | 4;3;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Pruning;block sparsity;structured sparsity;Recurrent Neural Networks;Speech Recognition | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 5.666667 | 5;5;7 | null | null | Block-Sparse Recurrent Neural Networks | null | null | 0 | 3.666667 | Reject | 4;3;4 | null |
null | Princeton University; MIT; University of Toronto; Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B Tenenbaum, Hugo Larochelle, Richard Zemel | https://iclr.cc/virtual/2018/poster/88 | Few-shot learning;semi-supervised learning;meta-learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 6;6;6 | null | null | Meta-Learning for Semi-Supervised Few-Shot Classification | null | null | 0 | 4.333333 | Poster | 4;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Privacy-preserving deep learning;Neural network training | null | 0 | null | null | iclr | 0.755929 | 0 | null | main | 4.666667 | 3;5;6 | null | null | PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training | null | null | 0 | 3.666667 | Reject | 3;3;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | CNN;ensemble;image recognition | null | 0 | null | null | iclr | -1 | 0 | null | main | 4.333333 | 4;4;5 | null | null | CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble Behavior | null | null | 0 | 4.666667 | Reject | 5;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | Yang Fan, Fei Tian, Tao Qin, Tie-Yan Liu | https://iclr.cc/virtual/2018/poster/193 | null | null | 0 | null | null | iclr | -0.693375 | 0 | null | main | 7.333333 | 5;8;9 | null | null | Learning to Teach | null | null | 0 | 3.666667 | Poster | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 3.666667 | 3;4;4 | null | null | Bit-Regularized Optimization of Neural Nets | null | null | 0 | 4.333333 | Reject | 4;5;4 | null |
null | Stanford University; Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | Nishal Shah, Sasidhar Madugula, E.J. Chichilnisky, Yoram Singer, Jonathon Shlens | https://iclr.cc/virtual/2018/poster/206 | Metric learning;Computational Neuroscience;Retina;Neural Prosthesis | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 5;6;7 | null | null | Learning a neural response metric for retinal prosthesis | null | null | 0 | 3.666667 | Poster | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | 0.755929 | 0 | null | main | 5.666667 | 5;6;6 | null | null | Better Generalization by Efficient Trust Region Method | null | null | 0 | 3.333333 | Reject | 2;3;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep reinforcement learning;maximum entropy learning;stochastic actor-critic | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 3;5;7 | null | null | Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor | null | null | 0 | 4 | Workshop | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | asynchronous;neural network;deep learning;graph;tree;rnn | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 5.333333 | 4;6;6 | null | null | AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks | null | null | 0 | 4.666667 | Reject | 5;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Word2Vec;Word Mover's Distance;Document Embedding | null | 0 | null | null | iclr | 0 | 0 | null | main | 0 | null | null | null | Word Mover's Embedding: From Word2Vec to Document Embedding | null | null | 0 | 0 | Active | null | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Classification;Feature Combination;Feature Mapping;Feed-Forward Neural Network;Genetic Algorithm;Linear Transfer Function;Non-Linear Transfer Function | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 2 | 1;2;3 | null | null | ENRICHMENT OF FEATURES FOR CLASSIFICATION USING AN OPTIMIZED LINEAR/NON-LINEAR COMBINATION OF INPUT FEATURES | null | null | 0 | 4 | Reject | 5;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | human in the loop;GANs;generative adversarial networks;image generative models;computer vision | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 4.333333 | 4;4;5 | null | null | Improving image generative models with human interactions | null | null | 0 | 4 | Reject | 4;5;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | adversarial;video prediction;flow | null | 0 | null | null | iclr | 0.333333 | 0 | https://sites.google.com/site/omvideoprediction | main | 4 | 3;3;3;7 | null | null | Self-Supervised Learning of Object Motion Through Adversarial Video Prediction | null | null | 0 | 4.75 | Reject | 5;4;5;5 | null |
null | University of Amsterdam; Now at Bethgelab, University of T ¨ubingen; University of Amsterdam; CVN, CentraleSup ´elec, Universit ´e Paris-Saclay; Galen team, INRIA Saclay; SequeL team, INRIA Lille; DI, ENS, Universit ´e PSL | 2018 | 0 | null | null | 0 | null | null | null | null | null | Joern-Henrik Jacobsen, Arnold W Smeulders, Edouard Oyallon | https://iclr.cc/virtual/2018/poster/103 | null | null | 0 | null | null | iclr | 0 | 0 | null | main | 8.333333 | 8;8;9 | null | null | i-RevNet: Deep Invertible Networks | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | factorization;general-purpose methods | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 3.666667 | 3;4;4 | null | null | Structured Deep Factorization Machine: Towards General-Purpose Architectures | null | null | 0 | 4.333333 | Reject | 5;5;3 | null |
null | Baidu Research; University of California, Berkeley; OpenAI | 2018 | 0 | null | null | 0 | null | null | null | null | null | Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan Arik, Ajay Kannan, SHARAN NARANG, Jonathan Raiman, John Miller | https://iclr.cc/virtual/2018/poster/323 | 2000-Speaker Neural TTS;Monotonic Attention;Speech Synthesis | null | 0 | null | null | iclr | 0 | 0 | null | main | 6.333333 | 6;6;7 | null | null | Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning | null | null | 0 | 4 | Poster | 3;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | hyperparameters;optimization;SGD;Adam;Bayesian | null | 0 | null | null | iclr | 0 | 0 | null | main | 4.333333 | 4;4;5 | null | null | Dynamically Learning the Learning Rates: Online Hyperparameter Optimization | null | null | 0 | 4 | Withdraw | 4;4;4 | null |
null | Department of Biology, University of Utrecht, The Netherlands; Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands | 2018 | 0 | null | null | 0 | null | null | null | null | null | Bojian Yin, Marleen Balvert, Davide Zambrano, Alexander Schoenhuth, Sander Bohte | https://iclr.cc/virtual/2018/poster/229 | DNA sequences;Hilbert curves;Convolutional neural networks;chromatin structure | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 7;7;7 | null | null | An image representation based convolutional network for DNA classification | null | null | 0 | 4.333333 | Poster | 5;3;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | generative models;probabilistic modelling;reinforcement learning;state-space models;planning | null | 0 | null | null | iclr | 0 | 0 | null | main | 6.333333 | 5;6;8 | null | null | Learning Dynamic State Abstractions for Model-Based Reinforcement Learning | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | Xingyu Liu, Jeff Pool, song han, Bill Dally | https://iclr.cc/virtual/2018/poster/297 | deep learning;convolutional neural network;pruning | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 7.333333 | 7;7;8 | null | null | Efficient Sparse-Winograd Convolutional Neural Networks | null | null | 0 | 3.666667 | Poster | 3;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | the manifold assumption;adversarial perturbation;neural networks | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 3;4;5 | null | null | The Manifold Assumption and Defenses Against Adversarial Perturbations | null | null | 0 | 3 | Reject | 3;3;3 | null |
null | University of Basel, Switzerland | 2018 | 0 | null | null | 0 | null | null | null | null | null | Aleksander Wieczorek, Mario Wieser, Damian Murezzan, Volker Roth | https://iclr.cc/virtual/2018/poster/156 | Information Bottleneck;Deep Information Bottleneck;Deep Variational Information Bottleneck;Variational Autoencoder;Sparsity;Disentanglement;Interpretability;Copula;Mutual Information | null | 0 | null | null | iclr | -0.662266 | 0 | null | main | 5.75 | 5;6;6;6 | null | null | Learning Sparse Latent Representations with the Deep Copula Information Bottleneck | null | null | 0 | 2.75 | Poster | 4;1;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Ensemble learning;neural networks | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 6;6;6 | null | null | Coupled Ensembles of Neural Networks | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | Facebook AI Research; Cornell University; Tsinghua University; Fudan University | 2018 | 0 | null | null | 0 | null | null | null | null | null | Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, Kilian Weinberger | https://iclr.cc/virtual/2018/poster/278 | efficient learning;budgeted learning;deep learning;image classification;convolutional networks | null | 0 | null | null | iclr | 0 | 0 | null | main | 8.333333 | 7;8;10 | null | null | Multi-Scale Dense Networks for Resource Efficient Image Classification | null | null | 0 | 4 | Oral | 4;4;4 | null |
null | Osaro Inc.; EECS Department, UC Berkeley | 2018 | 0 | null | null | 0 | null | null | null | null | null | Aviv Tamar, Khashayar Rohanimanesh, Yinlam Chow, Chris Vigorito, Ben Goodrich, Michael Kahane, Derik Pridmore | https://iclr.cc/virtual/2018/poster/53 | multi-modal imitation learning;deep learning;generative models;stochastic neural networks | null | 0 | null | null | iclr | 1 | 0 | https://vimeo.com/240212286/fd401241b9 | main | 5.333333 | 4;6;6 | null | null | Imitation Learning from Visual Data with Multiple Intentions | null | null | 0 | 3.666667 | Poster | 3;4;4 | null |
null | Department of Engineering Science, University of Oxford; Department of Engineering Science, University of Oxford and Alan Turing Institute | 2018 | 0 | null | null | 0 | null | null | null | null | null | Leonard Berrada, Andrew Zisserman, M. Pawan Kumar | https://iclr.cc/virtual/2018/poster/170 | null | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 7 | 6;7;8 | null | null | Smooth Loss Functions for Deep Top-k Classification | null | null | 0 | 4.333333 | Poster | 5;4;4 | null |
null | DeepMind, London, UK; Department of Computer Science and Technology, University of Cambridge, UK | 2018 | 0 | null | null | 0 | null | null | null | null | null | Kris Cao, Angeliki Lazaridou, Marc Lanctot, Joel Z Leibo, Karl Tuyls, Stephen Clark | https://iclr.cc/virtual/2018/poster/210 | multi-agent learning;reinforcement learning;game theory;emergent communication | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 5;6;7 | null | null | Emergent Communication through Negotiation | null | null | 0 | 3.666667 | Poster | 4;3;4 | null |
null | Departments of Electrical Engineering and Statistics, Stanford University; Departments of Management Science & Engineering, Stanford University; Departments of Electrical Engineering, Stanford University | 2018 | 0 | null | null | 0 | null | null | null | null | null | Aman Sinha, Hong Namkoong, John Duchi | https://iclr.cc/virtual/2018/poster/147 | adversarial training;distributionally robust optimization;deep learning;optimization;learning theory | null | 0 | null | null | iclr | 0 | 0 | null | main | 9 | 9;9;9 | null | null | Certifying Some Distributional Robustness with Principled Adversarial Training | null | null | 0 | 4.333333 | Oral | 4;4;5 | null |
null | Department of Computer Science, Princeton University, Princeton, NJ 08544, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Brian Bullins, Cyril Zhang, Yi Zhang | https://iclr.cc/virtual/2018/poster/283 | kernel learning;random features;online learning | null | 0 | null | null | iclr | -0.755929 | 0 | null | main | 5.666667 | 4;6;7 | null | null | Not-So-Random Features | null | null | 0 | 4.333333 | Poster | 5;5;3 | null |
null | University of California, Berkeley | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Prior knowledge;Reinforcement learning;Cognitive Science | null | 0 | null | null | iclr | 0.755929 | 0 | null | main | 5.333333 | 4;5;7 | null | null | Investigating Human Priors for Playing Video Games | null | null | 0 | 3.666667 | Workshop | 3;4;4 | null |
null | NVIDIA and Aalto University; NVIDIA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen | https://iclr.cc/virtual/2018/poster/204 | generative adversarial networks;unsupervised learning;hierarchical methods | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.666667 | 1;8;8 | null | null | Progressive Growing of GANs for Improved Quality, Stability, and Variation | null | null | 0 | 4 | Oral | 4;4;4 | null |
null | Microsoft Research, Redmond; Google; Computer Science, Lehigh University; Computer Science, Dartmouth College | 2018 | 0 | null | null | 0 | null | null | null | null | null | Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He | https://iclr.cc/virtual/2018/poster/248 | generative adversarial network;discrimination;generalization | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.333333 | 3;6;7 | null | null | On the Discrimination-Generalization Tradeoff in GANs | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | Salesforce Research, Palo Alto, CA 94301, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Romain Paulus, Caiming Xiong, richard socher | https://iclr.cc/virtual/2018/poster/279 | deep learning;natural language processing;reinforcement learning;text summarization;sequence generation | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 7 | 6;7;8 | null | null | A Deep Reinforced Model for Abstractive Summarization | null | null | 0 | 4 | Poster | 4;5;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep learning;deep reinforcement learning;combinatorial games;optimality | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.666667 | 5;6;6 | null | null | Can Deep Reinforcement Learning solve Erdos-Selfridge-Spencer Games? | null | null | 0 | 3 | Workshop | 3;3;3 | null |
null | Google Inc.; Georgia Tech | 2018 | 0 | null | null | 0 | null | null | null | null | null | Kevin Murphy | https://iclr.cc/virtual/2018/poster/218 | variational autoencoders;generative models;language;vision;abstraction;compositionality;hierarchy | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 7;7;7 | null | null | Generative Models of Visually Grounded Imagination | null | null | 0 | 3.333333 | Poster | 3;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 4.333333 | 4;4;5 | null | null | 3C-GAN: AN CONDITION-CONTEXT-COMPOSITE GENERATIVE ADVERSARIAL NETWORKS FOR GENERATING IMAGES SEPARATELY | null | null | 0 | 4.666667 | Reject | 5;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | complex numbers;complex-valued;neural;network;multi-layer;perceptron;architecture | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 3 | 2;3;4 | null | null | Complex- and Real-Valued Neural Network Architectures | null | null | 0 | 4.333333 | Withdraw | 5;4;4 | null |
null | RIKEN Center for Advanced Intelligence Project, Tokyo, Japan; SKOLTECH, Moscow, Russia & RIKEN BSI, Japan; RIKEN Center for Advanced Intelligence Project & Saitama Institute of Technology, Japan; RIKEN Center for Advanced Intelligence Project & The University of Tokyo, Tokyo, Japan | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Tensor Decomposition;Tensor Networks;Stochastic Gradient Descent | null | 0 | null | null | iclr | -1 | 0 | null | main | 5.333333 | 5;5;6 | null | null | Learning Efficient Tensor Representations with Ring Structure Networks | null | null | 0 | 3.666667 | Workshop | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.666667 | 5;5;7 | null | null | Auxiliary Guided Autoregressive Variational Autoencoders | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Reinforcement Learning;Chemistry;Interpretable Models | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 5;5;5 | null | null | Using Deep Reinforcement Learning to Generate Rationales for Molecules | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | MPI for Intelligent Systems, Tübingen, Germany; Google Brain, Zürich, Switzerland | 2018 | 0 | null | null | 0 | null | null | null | null | null | Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schoelkopf | https://iclr.cc/virtual/2018/poster/182 | auto-encoder;generative models;GAN;VAE;unsupervised learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 8 | 8;8;8 | null | null | Wasserstein Auto-Encoders | null | null | 0 | 3.333333 | Oral | 3;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep learning;Theory | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 3;4;5 | null | null | Improving Deep Learning by Inverse Square Root Linear Units (ISRLUs) | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Quantum Annealing;Reinforcement Learning;Boltzmann Machines;Markov Chain Monte Carlo | null | 0 | null | null | iclr | 1 | 0 | null | main | 4.666667 | 4;4;6 | null | null | Reinforcement Learning via Replica Stacking of Quantum Measurements for the Training of Quantum Boltzmann Machines | null | null | 0 | 3.333333 | Reject | 3;3;4 | null |
null | Electrical Engineering Department, University of South Florida, Tampa, FL 33620 | 2018 | 0 | null | null | 0 | null | null | null | null | null | Ozsel Kilinc, Ismail Uysal | https://iclr.cc/virtual/2018/poster/250 | representation learning;unsupervised clustering;pseudo supervision;graph-based activity regularization;auto-clustering output layer | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization | null | null | 0 | 4 | Poster | 5;4;3 | null |
null | The Hebrew University of Jerusalem | 2018 | 0 | null | null | 0 | null | null | null | null | null | Or Sharir, Amnon Shashua | https://iclr.cc/virtual/2018/poster/230 | Deep Learning;Expressive Efficiency;Overlapping;Receptive Fields | null | 0 | null | null | iclr | -1 | 0 | null | main | 6.666667 | 6;6;8 | null | null | On the Expressive Power of Overlapping Architectures of Deep Learning | null | null | 0 | 3.666667 | Poster | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | graph classification;convolutional neural networks;2D CNN;representation | null | 0 | null | null | iclr | -0.693375 | 0 | null | main | 4.666667 | 3;4;7 | null | null | Graph Classification with 2D Convolutional Neural Networks | null | null | 0 | 3.666667 | Reject | 5;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | 1 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Combining Model-based and Model-free RL via Multi-step Control Variates | null | null | 0 | 3.666667 | Reject | 3;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | 0 | 0 | null | main | 2.666667 | 1;3;4 | null | null | Improved Learning in Convolutional Neural Networks with Shifted Exponential Linear Units (ShELUs) | null | null | 0 | 5 | Withdraw | 5;5;5 | null |
null | School of Engineering, University of Guelph, Canada; Canadian Institute for Advanced Research; Vector Institute for Artificial Intelligence, Canada; School of Engineering, University of Guelph, Canada | 2018 | 0 | null | null | 0 | null | null | null | null | null | Angus Galloway, Graham W Taylor, Medhat Moussa | https://iclr.cc/virtual/2018/poster/47 | adversarial examples;adversarial attacks;binary;binarized neural networks | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Attacking Binarized Neural Networks | https://github.com/AngusG/cleverhans-attacking-bnns | null | 0 | 4 | Poster | 5;4;3 | null |
null | Université de Montréal & Montreal Institute for Learning Algorithms (MILA); Canadian Institute for Advanced Research (CIFAR); Département d’informatique de l’ENS, Paris, France; INRIA, École normale supérieure, CNRS, PSL Research University; National Research University Higher School of Economics, Moscow, Russia; Département d’informatique de l’ENS, Paris, France; INRIA, École normale supérieure, CNRS, PSL Research University | 2018 | 0 | null | null | 0 | null | null | null | null | null | Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin, Simon Lacoste-Julien | https://iclr.cc/virtual/2018/poster/191 | Structured prediction;RNNs | null | 0 | null | null | iclr | -0.654654 | 0 | null | main | 6.666667 | 5;7;8 | null | null | SEARNN: Training RNNs with global-local losses | null | null | 0 | 4 | Poster | 5;3;4 | null |
null | ETH Zurich, KU Leuven; ETH Zurich, Merantix; ETH Zurich | 2018 | 0 | null | null | 0 | null | null | null | null | null | Róbert Torfason, Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool | https://iclr.cc/virtual/2018/poster/21 | null | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 7 | 6;6;9 | null | null | Towards Image Understanding from Deep Compression Without Decoding | null | null | 0 | 4 | Poster | 3;4;5 | null |
null | QUV A Lab, University of Amsterdam, Amsterdam, Netherlands | 2018 | 0 | null | null | 0 | null | null | null | null | null | Peter OConnor, Efstratios Gavves, Matthias Reisser, Max Welling | https://iclr.cc/virtual/2018/poster/135 | online learning;spiking networks;deep learning;temporal | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 6;7;8 | null | null | Temporally Efficient Deep Learning with Spikes | null | null | 0 | 4.333333 | Poster | 4;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 4;5;6 | null | null | DENSELY CONNECTED RECURRENT NEURAL NETWORK FOR SEQUENCE-TO-SEQUENCE LEARNING | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Applied deep learning;Image segmentation;Hyperspectral Imaging;Feature sampling | null | 0 | null | null | iclr | 0 | 0 | null | main | 4.333333 | 3;4;6 | null | null | Data-driven Feature Sampling for Deep Hyperspectral Classification and Segmentation | null | null | 0 | 5 | Reject | 5;5;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 3.666667 | 3;3;5 | null | null | Convolutional Normalizing Flows | null | null | 0 | 4.333333 | Reject | 5;4;4 | null |
null | EPFL; University of Amsterdam & CIFAR; University of Amsterdam | 2018 | 0 | null | null | 0 | null | null | null | null | null | Taco Cohen, Mario Geiger, Jonas Koehler, Max Welling | https://iclr.cc/virtual/2018/poster/144 | deep learning;equivariance;convolution;group convolution;3D;vision;omnidirectional;shape recognition;molecular energy regression | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 8 | 7;8;9 | null | null | Spherical CNNs | null | null | 0 | 3.666667 | Oral | 3;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 4.333333 | 4;4;5 | null | null | The Mutual Autoencoder: Controlling Information in Latent Code Representations | null | null | 0 | 4.333333 | Reject | 5;4;4 | null |
null | Google | 2018 | 0 | null | null | 0 | null | null | null | null | null | Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V Le, Jeff Dean | https://iclr.cc/virtual/2018/poster/140 | deep learning;device placement;policy gradient optimization | null | 0 | null | null | iclr | 1 | 0 | null | main | 6 | 5;5;8 | null | null | A Hierarchical Model for Device Placement | null | null | 0 | 4.333333 | Poster | 4;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | reinforcement learning;molecule design;de novo design;ppo;sample-efficient reinforcement learning | null | 0 | null | null | iclr | 0.981981 | 0 | null | main | 5.666667 | 4;6;7 | null | null | Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design | null | null | 0 | 3 | Workshop | 2;3;4 | null |