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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | CNN;sparse convolution;sparse kernel;sparsity;model utilization;image classification | null | 0 | null | null | iclr | 1 | 0 | null | main | 5.333333 | 5;5;6 | null | null | Sparse-Complementary Convolution for Efficient Model Utilization on CNNs | null | null | 0 | 4.333333 | Reject | 4;4;5 | null |
null | Affiliation (Please provide the actual affiliation(s) if available); Another Affiliation (Please provide the actual affiliation(s) if available) | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep learning;symbolic knowledge;semi-supervised learning;constraints | null | 0 | null | null | iclr | -0.755929 | 0 | (Please provide the project link if available) | main | 5.333333 | 4;5;7 | null | null | A Semantic Loss Function for Deep Learning with Symbolic Knowledge | (Please provide the GitHub link if available) | null | 0 | 3.333333 | Reject | 4;3;3 | null |
null | Department of Computer Science, Rice University, Houston, TX 77005, USA. | 2018 | 0 | null | null | 0 | null | null | null | null | null | Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine | https://iclr.cc/virtual/2018/poster/89 | Program generation;Source code;Program synthesis;Deep generative models | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 7.333333 | 7;7;8 | null | null | Neural Sketch Learning for Conditional Program Generation | null | null | 0 | 3 | Oral | 3;2;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | neural networks;theory;optimization;local minima;loss landscape | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 6 | 5;6;7 | null | null | Exponentially vanishing sub-optimal local minima in multilayer neural networks | null | null | 0 | 2.666667 | Workshop | 3;3;2 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | Vadim Popov, Mikhail Kudinov, Irina Piontkovskaya, Petr Vytovtov, Alex Nevidomsky | https://iclr.cc/virtual/2018/poster/326 | distributed training;federated learning;language modeling;differential privacy | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 4.333333 | 4;4;5 | null | null | Distributed Fine-tuning of Language Models on Private Data | null | null | 0 | 3.666667 | Poster | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | adversarial examples;neural networks;formal verification;ground truths | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 5 | 4;5;6 | null | null | Ground-Truth Adversarial Examples | null | null | 0 | 3.666667 | Reject | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | neural network;quantization;compression | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 6;6;6 | null | null | Automatic Parameter Tying in Neural Networks | null | null | 0 | 4.333333 | Reject | 5;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | survival analysis;competing risks;siamese neural networks | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 4;4;4 | null | null | Siamese Survival Analysis with Competing Risks | null | null | 0 | 4.333333 | Reject | 5;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 | 4.333333 | 3;4;6 | null | null | UPS: optimizing Undirected Positive Sparse graph for neural graph filtering | null | null | 0 | 3 | Reject | 3;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | semantic segmentation;conditional denoising autoencoders;iterative inference | null | 0 | null | null | iclr | 1 | 0 | null | main | 4.333333 | 4;4;5 | null | null | Image Segmentation by Iterative Inference from Conditional Score Estimation | null | null | 0 | 4.333333 | Reject | 4;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | visualization;loss surface;flatness;sharpness | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Visualizing the Loss Landscape of Neural Nets | null | null | 0 | 3.333333 | Workshop | 3;4;3 | null |
null | TU Berlin; Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | Pieter-Jan Kindermans, Kristof T Schütt, Maximilian Alber, Klaus R Muller, Dumitru Erhan, Been Kim, Sven Dähne | https://iclr.cc/virtual/2018/poster/322 | machine learning;interpretability;deep learning | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 7.333333 | 6;8;8 | null | null | Learning how to explain neural networks: PatternNet and PatternAttribution | null | null | 0 | 3.666667 | Poster | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | adversarial examples;generative adversarial network;black-box attack | null | 0 | null | null | iclr | -0.755929 | 0 | null | main | 5.666667 | 4;6;7 | null | null | Generating Adversarial Examples with Adversarial Networks | null | null | 0 | 3.666667 | Reject | 4;4;3 | null |
null | Google Brain, Mountain View, CA, 94043, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Chung-Cheng Chiu, Colin Raffel | https://iclr.cc/virtual/2018/poster/211 | attention;sequence-to-sequence;speech recognition;document summarization | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 6;7;8 | null | null | Monotonic Chunkwise Attention | null | null | 0 | 4.333333 | Poster | 4;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Bayesian;Deep Learning;Recurrent Neural Networks;LSTM | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 5.666667 | 5;6;6 | null | null | Revisiting Bayes by Backprop | null | null | 0 | 4.333333 | Reject | 4;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Reinforcement Learning;Lambda-Returns | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 5.333333 | 5;5;6 | null | null | Learning to Mix n-Step Returns: Generalizing Lambda-Returns for Deep Reinforcement Learning | null | null | 0 | 3.666667 | Reject | 4;3;4 | null |
null | Carnegie Mellon University; University of California, Berkeley | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep learning;MLP;ResNet;residual network;exploding gradient problem;vanishing gradient problem;effective depth;batch normalization;covariate shift | null | 0 | null | null | iclr | -0.433555 | 0 | null | main | 5.333333 | 3;5;8 | null | null | Gradients explode - Deep Networks are shallow - ResNet explained | null | null | 0 | 2.333333 | Workshop | 2;4;1 | 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 | Embedding Deep Networks into Visual Explanations | null | null | 0 | 0 | Withdraw | null | null |
null | California Institute of Technology, Pasadena, CA; University of California, Irvine, CA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Forough Arabshahi, Sameer Singh, anima anandkumar | https://iclr.cc/virtual/2018/poster/110 | symbolic reasoning;mathematical equations;recursive neural networks;neural programing | null | 0 | null | null | iclr | -0.944911 | 0 | null | main | 6.333333 | 5;6;8 | null | null | Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs | 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 | 0 | null | main | 4.333333 | 4;4;5 | null | null | withdrawn | null | null | 0 | 4 | Withdraw | 4;4;4 | null |
null | University of Illinois at Urbana-Champaign; Google; Microsoft Research; Citadel | 2018 | 0 | null | null | 0 | null | null | null | null | null | Po-Sen Huang, Chong Wang, Sitao Huang, Dengyong Zhou, Li Deng | https://iclr.cc/virtual/2018/poster/306 | Neural Machine Translation;Sequence to Sequence;Sequence Modeling | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 6.666667 | 6;6;8 | null | null | Towards Neural Phrase-based Machine Translation | https://github.com/posenhuang/NPMT | null | 0 | 4 | Poster | 3;4;5 | null |
null | Georgia State University; Georgia Institute of Technology | 2018 | 0 | null | null | 0 | null | null | null | null | null | Jiachen Yang, Xiaojing Ye, Rakshit Trivedi, huan xu, Hongyuan Zha | https://iclr.cc/virtual/2018/poster/268 | mean field games;reinforcement learning;Markov decision processes;inverse reinforcement learning;deep learning;inverse optimal control;computational social science;population modeling | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 8.666667 | 8;8;10 | null | null | Learning Deep Mean Field Games for Modeling Large Population Behavior | null | null | 0 | 4 | Oral | 4;3;5 | null |
null | GRASP Laboratory, University of Pennsylvania | 2018 | 0 | null | null | 0 | null | null | null | null | null | Carlos Esteves, Christine Allen-Blanchette, Xiaowei Zhou, Kostas Daniilidis | https://iclr.cc/virtual/2018/poster/263 | equivariance;invariance;canonical coordinates | null | 0 | null | null | iclr | -1 | 0 | null | main | 7.333333 | 7;7;8 | null | null | Polar Transformer Networks | http://github.com/daniilidis-group//polar-transformer-networks | null | 0 | 3.666667 | Poster | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | reinforcement learning;inverse reinforcement learning;imitation learning | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 4 | 3;4;5 | null | null | Reward Estimation via State Prediction | null | null | 0 | 3.666667 | Reject | 4;4;3 | null |
null | University of Illinois at Urbana Champaign | 2018 | 0 | null | null | 0 | null | null | null | null | null | Jiaqi Mu, Pramod Viswanath | https://iclr.cc/virtual/2018/poster/298 | null | null | 0 | null | null | iclr | -1 | 0 | null | main | 6.666667 | 6;7;7 | null | null | All-but-the-Top: Simple and Effective Postprocessing for Word Representations | null | null | 0 | 4.333333 | Poster | 5;4;4 | null |
null | Baylor College of Medicine & Rice University | 2018 | 0 | null | null | 0 | null | null | null | null | null | Emin Orhan, Xaq Pitkow | https://iclr.cc/virtual/2018/poster/73 | deep learning;optimization;skip connections | null | 0 | null | null | iclr | -1 | 0 | null | main | 7.333333 | 6;8;8 | null | null | Skip Connections Eliminate Singularities | null | null | 0 | 3.333333 | Poster | 4;3;3 | null |
null | Universitat Polit `ecnica de Catalunya; Barcelona Supercomputing Center; Columbia University; Google Inc | 2018 | 0 | null | null | 0 | null | null | null | null | null | Víctor Campos, Brendan Jou, Xavier Giro-i-Nieto, Jordi Torres, Shih-Fu Chang | https://iclr.cc/virtual/2018/poster/312 | recurrent neural networks;dynamic learning;conditional computation | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 6;6;6 | null | null | Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks | https://imatge-upc.github.io/skiprnn-2017-telecombcn/ | null | 0 | 4 | Poster | 4;4;4 | null |
null | School of Computer Science, Carnegie Mellon University | 2018 | 0 | null | null | 0 | null | null | null | null | null | Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, William W Cohen | https://iclr.cc/virtual/2018/poster/70 | null | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 7.333333 | 7;7;8 | null | null | Breaking the Softmax Bottleneck: A High-Rank RNN Language Model | https://github.com/zihangdai/mos | null | 0 | 4.333333 | Oral | 5;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Generative Modeling;Generative Adversarial Networks;Density Estimation | null | 0 | null | null | iclr | 0 | 0 | null | main | 3 | 3;3;3 | null | null | WHAT ARE GANS USEFUL FOR? | null | null | 0 | 4.666667 | Reject | 5;4;5 | null |
null | NVIDIA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Feiwen Zhu, Jeff Pool, Michael Andersch, Jeremy Appleyard, Fung Xie | https://iclr.cc/virtual/2018/poster/200 | Sparsity;Pruning;Compression;RNN;LSTM;Persistent;RF-Resident;GPU | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 6;6;6 | null | null | Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip | null | null | 0 | 2.666667 | Poster | 2;2;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep learning;Incremental learning;energy-efficient learning;supervised learning | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 3.333333 | 2;4;4 | null | null | Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing | null | null | 0 | 4.666667 | Withdraw | 5;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Generative Model of Graphs | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 5.666667 | 5;6;6 | null | null | Learning Deep Generative Models of Graphs | null | null | 0 | 3.333333 | Workshop | 3;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | machine reading;adversarial training | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Adversarial reading networks for machine comprehension | null | null | 0 | 4.666667 | Reject | 5;5;4 | null |
null | Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | David Ha, Douglas Eck | https://iclr.cc/virtual/2018/poster/293 | applications;image modelling;computer-assisted;drawing;art;creativity;dataset | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 5;8;8 | null | null | A Neural Representation of Sketch Drawings | https://magenta.tensorflow.org/sketch_rnn | null | 0 | 4 | Poster | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | novelty detection;GAN;feature matching;semi-supervised | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 4;5;6 | null | null | Novelty Detection with GAN | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | Department of Computer Science, University of Texas at Austin; Department of Electrical and Computer Engineering, University of Texas at Austin | 2018 | 0 | null | null | 0 | null | null | null | null | null | Ashish Bora, Eric Price, Alexandros Dimakis | https://iclr.cc/virtual/2018/poster/231 | Generative models;Adversarial networks;Lossy measurements | null | 0 | null | null | iclr | 0 | 0 | null | main | 7.333333 | 7;7;8 | null | null | AmbientGAN: Generative models from lossy measurements | null | null | 0 | 4 | Oral | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Predictive coding;deep neural network;generative model;unsupervised learning;learning latent representations | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 3.333333 | 3;3;4 | null | null | A Deep Predictive Coding Network for Learning Latent Representations | null | null | 0 | 4.333333 | Reject | 5;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | word representation;unsupervised learning;computational linguistics | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 4;5;6 | null | null | Tensor-Based Preposition Representation | null | null | 0 | 4 | Withdraw | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | hyperparameter optimization;random search;determinantal point processes;low discrepancy sequences | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 4;4;4 | null | null | Open Loop Hyperparameter Optimization and Determinantal Point Processes | null | null | 0 | 5 | Reject | 5;5;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | reinforcement learning;Markov decision processes;deep learning | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 4.333333 | 4;4;5 | null | null | Time Limits in Reinforcement Learning | null | null | 0 | 4.333333 | Reject | 5;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | generative adversarial networks;GANs;deep learning;unsupervised learning;generative models;adversarial learning | null | 0 | null | null | iclr | 1 | 0 | null | main | 4.666667 | 4;4;6 | null | null | Towards Effective GANs for Data Distributions with Diverse Modes | null | null | 0 | 3.666667 | Workshop | 3;3;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | recurrent neural networks;vanishing gradients;exploding gradients;orthogonal;unitary;long term dependencies;uRNN | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 6 | 5;6;7 | null | null | Orthogonal Recurrent Neural Networks with Scaled Cayley Transform | null | null | 0 | 3.333333 | Reject | 4;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Generalization;Reservoir Computing;dynamical system;Siamese Neural Network;image classification;similarity;dimensionality reduction | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 4;4;4 | null | null | Generalization of Learning using Reservoir Computing | null | null | 0 | 4 | Reject | 4;3;5 | null |
null | RIKEN Center for Advanced Intelligence Project, Tokyo, Japan; University of British Columbia, Vancouver, Canada | 2018 | 0 | null | null | 0 | null | null | null | null | null | Wu Lin, Nicolas Daniel Hubacher, Mohammad Emtiyaz Khan | https://iclr.cc/virtual/2018/poster/136 | Variational Inference;Variational Message Passing;Variational Auto-Encoder;Graphical Models;Structured Models;Natural Gradients | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 7;7;7 | null | null | Variational Message Passing with Structured Inference Networks | null | null | 0 | 3 | Poster | 4;3;2 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | language models;vector spaces;word embedding;similarity | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 3 | 2;3;4 | null | null | Comparison of Paragram and GloVe Results for Similarity Benchmarks | null | null | 0 | 4.333333 | Reject | 4;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep Learning;Neural Networks | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 5 | 4;5;6 | null | null | Neural Networks with Block Diagonal Inner Product Layers | null | null | 0 | 3.666667 | Reject | 3;4;4 | null |
null | Unknown Affiliation | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Adversarial Attacks;Unsupervised Defense;Deep Learning | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 5 | 3;5;7 | null | null | Towards Safe Deep Learning: Unsupervised Defense Against Generic Adversarial Attacks | 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 | 4.333333 | 3;5;5 | null | null | BinaryFlex: On-the-Fly Kernel Generation in Binary Convolutional Networks | null | null | 0 | 3.333333 | Reject | 4;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | multi-task learning;transfer learning | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 5 | 3;6;6 | null | null | Large Scale Multi-Domain Multi-Task Learning with MultiModel | null | null | 0 | 4 | Reject | 5;3;4 | null |
null | Department of Computer Science, University of Toronto, Toronto, Canada; DeepMind; Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | James Martens, Jimmy Ba, Matthew Johnson | https://iclr.cc/virtual/2018/poster/12 | optimization;K-FAC;natural gradient;recurrent neural networks | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 6.333333 | 5;7;7 | null | null | Kronecker-factored Curvature Approximations for Recurrent Neural Networks | null | null | 0 | 3.666667 | Poster | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Language Model;discriminative model | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 5.666667 | 5;5;7 | null | null | Large Margin Neural Language Models | null | null | 0 | 4.666667 | Reject | 4;5;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | supervised representation learning;causality;interpretability;transfer learning | null | 0 | null | null | iclr | -0.981981 | 0 | null | main | 5.333333 | 4;5;7 | null | null | Grouping-By-ID: Guarding Against Adversarial Domain Shifts | null | null | 0 | 4 | Reject | 5;4;3 | null |
null | School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Taesik Na, Jong Hwan Ko, Saibal Mukhopadhyay | https://iclr.cc/virtual/2018/poster/324 | adversarial machine learning;embedding;regularization;adversarial attack | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.666667 | 5;6;6 | null | null | Cascade Adversarial Machine Learning Regularized with a Unified Embedding | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | Google Research, New York, NY 10011, USA; Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Swabha Swayamdipta, Ankur Parikh, Tom Kwiatkowski | https://iclr.cc/virtual/2018/poster/95 | reading comprehension;multi-loss;question answering;scalable;TriviaQA;feed-forward;latent variable;attention | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 5;6;7 | null | null | Multi-Mention Learning for Reading Comprehension with Neural Cascades | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | Paper under double-blind review | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | NLP;morphology;seq2seq | null | 0 | null | null | iclr | -0.27735 | 0 | null | main | 4.333333 | 2;5;6 | null | null | Achieving morphological agreement with Concorde | null | null | 0 | 4.666667 | Reject | 5;4;5 | null |
null | Department of Computer Science, University of California, Berkeley, Berkeley, CA 94720, USA∗; eric@ericmart.in | 2018 | 0 | null | null | 0 | null | null | null | null | null | Eric Martin, Christopher Cundy | https://iclr.cc/virtual/2018/poster/249 | rnn;sequence;parallel;qrnn;sru;gilr;gilr-lstm | null | 0 | null | null | iclr | 0 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Parallelizing Linear Recurrent Neural Nets Over Sequence Length | null | null | 0 | 3 | Poster | 3;4;2 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | developmental robotics;intrinsic motivation;strategic learning;complex motor policies | null | 0 | null | null | iclr | 0 | 0 | null | main | 0 | null | null | null | Learning a set of interrelated tasks by using a succession of motor policies for a socially guided intrinsically motivated learner | null | null | 0 | 0 | Withdraw | null | null |
null | Computer Science, University of California, Los Angeles | 2018 | 0 | null | null | 0 | null | null | null | null | null | Pratik A Chaudhari, Stefano Soatto | https://iclr.cc/virtual/2018/poster/152 | sgd;variational inference;gradient noise;out-of-equilibrium | null | 0 | null | null | iclr | 0.944911 | 0 | null | main | 6.333333 | 5;6;8 | null | null | Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks | null | null | 0 | 4.333333 | Poster | 4;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | graph neural networks;ConvNets;RNNs;pattern matching;semi-supervised clustering | null | 0 | null | null | iclr | -0.27735 | 0 | null | main | 5.333333 | 3;6;7 | null | null | Residual Gated Graph ConvNets | null | null | 0 | 3.666667 | Reject | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | generative;hierarchical;unsupervised;semisupervised;latent;ALI;GAN | null | 0 | null | null | iclr | -1 | 0 | null | main | 5.666667 | 5;5;7 | null | null | Hierarchical Adversarially Learned Inference | null | null | 0 | 4.333333 | Reject | 5;5;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | soft Q-learning;policy gradients;entropy;Legendre transformation;duality;convex analysis;Donsker-Varadhan | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 4 | 2;5;5 | null | null | Representing Entropy : A short proof of the equivalence between soft Q-learning and policy gradients | null | null | 0 | 4.666667 | Reject | 5;5;4 | null |
null | Machine Intelligence and Perception, Microsoft Research, Cambridge, UK | 2018 | 0 | null | null | 0 | null | null | null | null | null | Sebastian Nowozin | https://iclr.cc/virtual/2018/poster/114 | variational inference;approximate inference;generative models | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference | https://github.com/Microsoft/jackknife-variational-inference | null | 0 | 3.333333 | Poster | 3;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep learning;representation learning;variational auto-encoders;variational inference;generative models | null | 0 | null | null | iclr | 0.944911 | 0 | null | main | 4.666667 | 3;5;6 | null | null | Feature Map Variational Auto-Encoders | null | null | 0 | 3.666667 | Reject | 3;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | one-shot learning;few-shot learning;Omniglot | null | 0 | null | null | iclr | 0 | 0 | null | main | 3.333333 | 3;3;4 | null | null | Gaussian Prototypical Networks for Few-Shot Learning on Omniglot | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | GRASP Laboratory, University of Pennsylvania | 2018 | 0 | null | null | 0 | null | null | null | null | null | Arbaaz Khan, Clark Zhang, Nikolay Atanasov, Konstantinos Karydis, Vijay Kumar, Daniel D Lee | https://iclr.cc/virtual/2018/poster/321 | planning;memory networks;deep learning;robotics | null | 0 | null | null | iclr | -0.216777 | 0 | null | main | 6.333333 | 4;6;9 | null | null | Memory Augmented Control Networks | null | null | 0 | 3.666667 | Poster | 5;2;4 | null |
null | Google Brain, Mountain View, CA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Peter J Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer | https://iclr.cc/virtual/2018/poster/121 | abstractive summarization;Transformer;long sequences;natural language processing;sequence transduction;Wikipedia;extractive summarization | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 7.333333 | 7;7;8 | null | null | Generating Wikipedia by Summarizing Long Sequences | null | null | 0 | 4 | Poster | 4;5;3 | null |
null | University of Freiburg; Intel Labs | 2018 | 0 | null | null | 0 | null | null | null | null | null | Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun, Thomas Brox | https://iclr.cc/virtual/2018/poster/190 | deep learning;reinforcement learning;temporal difference | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 7;7;7 | null | null | TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | Paper under double-blind review | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | stochastic gradient descent;autoencoders;nonconvex optimization;representation learning;theory | null | 0 | null | null | iclr | -1 | 0 | null | main | 2.333333 | 2;2;3 | null | null | Demystifying overcomplete nonlinear auto-encoders: fast SGD convergence towards sparse representation from random initialization | null | null | 0 | 3.666667 | Reject | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | automatic speech recognition;letter-based acoustic model;gated convnets | null | 0 | null | null | iclr | 0.188982 | 0 | null | main | 4.333333 | 3;4;6 | null | null | Gated ConvNets for Letter-Based ASR | null | null | 0 | 4.666667 | Reject | 5;4;5 | null |
null | University of California, Berkeley | 2018 | 0 | null | null | 0 | null | null | null | null | null | Chelsea Finn, Sergey Levine | https://iclr.cc/virtual/2018/poster/185 | meta-learning;learning to learn;universal function approximation | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 6.333333 | 6;6;7 | null | null | Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm | null | null | 0 | 1.666667 | Poster | 1;3;1 | null |
null | Toyota Central R&D Labs, Inc., Nagakute, Aichi 480-1192, Japan | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | vanishing gradient problem;multilayer perceptron;angle bias | null | 0 | null | null | iclr | 0 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Linearly Constrained Weights: Resolving the Vanishing Gradient Problem by Reducing Angle Bias | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep learning;transfer learning;adjacency matrices;image feature representation;Caffe;graph classification | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 3;6;6 | null | null | Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification | null | null | 0 | 3 | Reject | 3;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep Learning;Neural Networks;Information Theory;Generative models;GAN;Adversarial | null | 0 | null | null | iclr | 0 | 0 | null | main | 4.333333 | 3;5;5 | null | null | MINE: Mutual Information Neural Estimation | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Visual Relations;Visual Reasoning;SVRT;Attention;Working Memory;Convolutional Neural Network;Deep Learning;Relational Network | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 6;6;6 | null | null | Not-So-CLEVR: Visual Relations Strain Feedforward Neural Networks | null | null | 0 | 3.333333 | Workshop | 3;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | neural networks;autoencoder;generative;feed-back | null | 0 | null | null | iclr | -1 | 0 | null | main | 3.333333 | 3;3;4 | null | null | Style Memory: Making a Classifier Network Generative | null | null | 0 | 4.333333 | Reject | 5;5;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | vanishing gradient descent;recurrent neural networks;identity mapping | null | 0 | null | null | iclr | -0.114708 | 0 | null | main | 4.333333 | 2;4;7 | null | null | Overcoming the vanishing gradient problem in plain recurrent networks | null | null | 0 | 4.333333 | Reject | 4;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | CLEVR;VQA;Visual Question Answering;Neural Programmer | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.333333 | 5;5;6 | null | null | DDRprog: A CLEVR Differentiable Dynamic Reasoning Programmer | null | null | 0 | 2 | Reject | 2;2;2 | null |
null | Google Brain, Also at the Department of Computing Science, University of Alberta; Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans | https://iclr.cc/virtual/2018/poster/167 | Reinforcement learning | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 5.333333 | 5;5;6 | null | null | Trust-PCL: An Off-Policy Trust Region Method for Continuous Control | https://github.com/tensorflow/models/tree/master/research/pcl_rl | null | 0 | 3 | Poster | 4;1;4 | null |
null | Under double-blind review | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | subspace;censor;multi-task;deep network | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases | 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;Structured Variational Inference;Multi-agent Coordination;Multi-agent Learning | null | 0 | null | null | iclr | -0.755929 | 0 | null | main | 5.333333 | 4;5;7 | null | null | Structured Exploration via Hierarchical Variational Policy 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 | conditional sequence generation;generative adversarial network;REINFORCE;dialogue generation | null | 0 | null | null | iclr | 0 | 0 | null | main | 0 | null | null | null | Withdrawn | null | null | 0 | 0 | Withdraw | null | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | sign;stochastic;gradient;non-convex;optimization;gradient;quantization;convergence;rate | null | 0 | null | null | iclr | 1 | 0 | null | main | 4.333333 | 4;4;5 | null | null | Convergence rate of sign stochastic gradient descent for non-convex functions | null | null | 0 | 4.333333 | Reject | 4;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | quantum technique;convolution networks;shape detection | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 4 | 3;4;5 | null | null | Towards Quantum Inspired Convolution Networks | null | null | 0 | 3.666667 | Withdraw | 5;3;3 | null |
null | Yitu Tech; Shanghai Jiao Tong University; University College London | 2018 | 0 | null | null | 0 | null | null | null | null | null | Zhiming Zhou, Han Cai, Shu Rong, Yuxuan Song, Kan Ren, Weinan Zhang, Jun Wang, Yong Yu | https://iclr.cc/virtual/2018/poster/221 | Generative Adversarial Nets;GANs;Evaluation Metrics;Generative Model;Deep Learning;Adversarial Learning;Inception Score;AM Score | null | 0 | null | null | iclr | 0 | 0 | null | main | 6.666667 | 5;7;8 | null | null | Activation Maximization Generative Adversarial Nets | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | University of California, Berkeley, USA; Massachusetts Institute of Technology, MA, USA; University of Michigan, Ann Arbor, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, Dawn Song | https://iclr.cc/virtual/2018/poster/18 | adversarial examples;spatial transformation | null | 0 | null | null | iclr | 1 | 0 | null | main | 7.666667 | 7;7;9 | null | null | Spatially Transformed Adversarial Examples | null | null | 0 | 4.333333 | Poster | 4;4;5 | null |
null | Department of Engineering Science, University of Oxford | 2018 | 0 | null | null | 0 | null | null | null | null | null | Saumya Jetley, Nicholas A. Lord, Namhoon Lee, Philip Torr | https://iclr.cc/virtual/2018/poster/34 | deep learning;attention-aware representations;image classification;weakly supervised segmentation;domain shift;classifier generalisation;robustness to adversarial attack | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.666667 | 5;6;6 | null | null | Learn to Pay Attention | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Sentence Vectors;Vector Semantics;Automatic Summarization | null | 0 | null | null | iclr | 0 | 0 | null | main | 2.333333 | 2;2;3 | null | null | Exploring Sentence Vectors Through Automatic Summarization | 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 | 4.666667 | 4;5;5 | null | null | Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning | null | null | 0 | 3.666667 | Reject | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep Learning;Spectral Graph Convolutional Neural Networks | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 4;6;8 | null | null | CAYLEYNETS: SPECTRAL GRAPH CNNS WITH COMPLEX RATIONAL FILTERS | null | null | 0 | 3 | Reject | 3;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | tensor contraction;tensor regression;network compression;deep neural networks | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 4.666667 | 4;4;6 | null | null | Tensor Contraction & Regression Networks | null | null | 0 | 3.666667 | Reject | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | learning representation;clustering;loss | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Forced Apart: Discovering Disentangled Representations Without Exhaustive Labels | null | null | 0 | 4.333333 | Reject | 4;5;4 | null |
null | Google Brain, Mountain View, CA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Jacob Buckman, Aurko Roy, Colin Raffel, Ian Goodfellow | https://iclr.cc/virtual/2018/poster/120 | Adversarial examples;robust neural networks | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 6;6;6 | null | null | Thermometer Encoding: One Hot Way To Resist Adversarial Examples | null | null | 0 | 3.333333 | Poster | 4;4;2 | null |
null | Affiliation not provided | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | bioinformatics;multi-label classification;matching networks;prototypes;memory networks;attention | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 5;5;5 | null | null | Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification | null | null | 0 | 4 | Reject | 5;4;3 | null |
null | Search Algorithm Team, Alibaba Group, China; AI-LAB, Alibaba Group, China; Key Laboratory of Machine Perception (MOE), School of EECS, Peking University, China | 2018 | 0 | null | null | 0 | null | null | null | null | null | Chen Xu, Jianqiang Yao, Zhouchen Lin, Baigui Sun, Yuanbin Cao, Zhirong Wang, Hongbin Zha | https://iclr.cc/virtual/2018/poster/235 | Alternating Minimization;Quantized Recurrent Neural Network;Binary Search Tree | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 7.333333 | 7;7;8 | null | null | Alternating Multi-bit Quantization for Recurrent Neural Networks | null | null | 0 | 3.333333 | Poster | 4;2;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | abbas abdolmaleki, Jost Tobias Springenberg, Nicolas Heess, Yuval Tassa, Remi Munos | https://iclr.cc/virtual/2018/poster/9 | Reinforcement Learning;Variational Inference;Control | null | 0 | null | null | iclr | 0.240192 | 0 | null | main | 6 | 5;6;7 | null | null | Maximum a Posteriori Policy Optimisation | null | null | 0 | 3.333333 | Poster | 4;1;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null | iclr | 0.59604 | 0 | null | main | 6.333333 | 4;6;9 | null | null | Data Augmentation Generative Adversarial Networks | null | null | 0 | 4 | Workshop | 4;3;5 | 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 | Pseudo sequence based deep neural network compression | null | null | 0 | 0 | Withdraw | null | null |
null | Google | 2018 | 0 | null | null | 0 | null | null | null | null | null | Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei Wang. | https://iclr.cc/virtual/2018/poster/107 | machine translation;paraphrasing;question answering;reinforcement learning;agents | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 7 | 6;7;8 | null | null | Ask the Right Questions: Active Question Reformulation with Reinforcement Learning | null | null | 0 | 4 | Oral | 4;5;3 | null |
null | IBM Thomas J. Watson Research Center, USA; POSTECH, Department of Creative IT Engineering, Korea | 2018 | 0 | null | null | 0 | null | null | null | null | null | Dongsoo Lee, Daehyun Ahn, Taesu Kim, Pierce I Chuang, Jae-Joon Kim | https://iclr.cc/virtual/2018/poster/256 | pruning;sparse matrix;memory footprint;model size;model compression | null | 0 | null | null | iclr | -1 | 0 | null | main | 6.333333 | 6;6;7 | null | null | Viterbi-based Pruning for Sparse Matrix with Fixed and High Index Compression Ratio | null | null | 0 | 3.666667 | Poster | 4;4;3 | null |
null | Flowers Team, Inria and Ensta-ParisTech, France; Flowers Team, Inria, Ensta-ParisTech and UPMC, France | 2018 | 0 | null | null | 0 | null | null | null | null | null | Alexandre Péré, Sébastien Forestier, Olivier Sigaud, Pierre-Yves Oudeyer | https://iclr.cc/virtual/2018/poster/309 | exploration; autonomous goal setting; diversity; unsupervised learning; deep neural network | null | 0 | null | null | iclr | 1 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration | null | null | 0 | 3.333333 | Poster | 2;4;4 | null |