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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | task clustering;matrix completion;multi-task learning;few-shot learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Robust Task Clustering for Deep and Diverse Multi-Task and Few-Shot Learning | null | null | 0 | 4 | Withdraw | 4;4;4 | null |
null | Department of Electronics and Computer Science, University of Southampton | 2018 | 0 | null | null | 0 | null | null | null | null | null | Yan Zhang, Jonathon Hare, Adam Prugel-Bennett | https://iclr.cc/virtual/2018/poster/307 | visual question answering;vqa;counting | null | 0 | null | null | iclr | -1 | 0 | null | main | 5.333333 | 4;6;6 | null | null | Learning to Count Objects in Natural Images for Visual Question Answering | https://github.com/Cyanogenoid/vqa-counting | null | 0 | 3.333333 | Poster | 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 | 0 | null | main | 0 | null | null | null | THINK VISUALLY: QUESTION ANSWERING THROUGH VIRTUAL IMAGERY | null | null | 0 | 0 | Active | null | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | interpretability;generative adversarial networks | null | 0 | null | null | iclr | -0.240192 | 0 | null | main | 6.333333 | 4;7;8 | null | null | Thinking like a machine — generating visual rationales through latent space optimization | null | null | 0 | 3 | Reject | 3;4;2 | null |
null | University of British Columbia | 2018 | 0 | null | null | 0 | null | null | null | null | null | Glen Berseth, Cheng Xie, Paul Cernek, Michiel van de Panne | https://iclr.cc/virtual/2018/poster/300 | Reinforcement Learning;Distillation;Transfer Learning;Continual Learning | null | 0 | null | null | iclr | 1 | 0 | null | main | 6.333333 | 5;7;7 | null | null | Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control | null | null | 0 | 3.666667 | Poster | 3;4;4 | null |
null | Carnegie Mellon University; Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | squad;stanford question answering dataset;reading comprehension;attention;text convolutions;question answering | null | 0 | null | null | iclr | 0.654654 | 0 | null | main | 6.333333 | 5;6;8 | null | null | QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension | null | null | 0 | 4 | Poster | 4;3;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Non-convex optimization;Deep Learning | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 5.333333 | 4;6;6 | null | null | No Spurious Local Minima in a Two Hidden Unit ReLU Network | null | null | 0 | 3 | Workshop | 4;3;2 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | boosting learning;deep learning;neural network | null | 0 | null | null | iclr | -0.960769 | 0 | null | main | 4.333333 | 2;5;6 | null | null | Deep Boosting of Diverse Experts | null | null | 0 | 4 | Reject | 5;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | reinforcement learning;safe exploration;dqn | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 5.666667 | 5;5;7 | null | null | Avoiding Catastrophic States with Intrinsic Fear | null | null | 0 | 4 | Reject | 4;5;3 | null |
null | Columbia University, New York, NY 10027, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Christopher Cueva, Xue-Xin Wei | https://iclr.cc/virtual/2018/poster/245 | recurrent neural network;grid cell;neural representation of space | null | 0 | null | null | iclr | 0 | 0 | null | main | 8.333333 | 8;8;9 | null | null | Emergence of grid-like representations by training recurrent neural networks to perform spatial localization | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | Microsoft Research Montreal; Montréal Institute for Learning Algorithms (MILA), Université de Montréal, CIFAR Senior Fellow; Montréal Institute for Learning Algorithms (MILA), Université de Montréal, Work done while author was an intern at Microsoft Research Montreal; Montréal Institute for Learning Algorithms (MILA), Ecole Polytechnique de Montréal | 2018 | 0 | null | null | 0 | null | null | null | null | null | Sandeep Subramanian, Adam Trischler, Yoshua Bengio, Christopher Pal | https://iclr.cc/virtual/2018/poster/99 | distributed sentence representations;multi-task learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 6.666667 | 4;8;8 | null | null | Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning | null | null | 0 | 5 | Poster | 5;5;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | clustering;deep learning;neural networks | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 2.666667 | 2;3;3 | null | null | Clustering with Deep Learning: Taxonomy and New Methods | null | null | 0 | 4.666667 | Reject | 5;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | NLU;word embeddings;representation learning | null | 0 | null | null | iclr | -1 | 0 | null | main | 5.666667 | 5;5;7 | null | null | Learning to Compute Word Embeddings On the Fly | null | null | 0 | 3.666667 | Reject | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | program synthesis;program induction;example selection | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Learning to select examples for program synthesis | null | null | 0 | 3.666667 | Reject | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | Anonymous | null | Deep Learning;Autoencoders;Alternating Optimization | null | 0 | null | null | iclr | 0.755929 | 0 | null | main | 5.666667 | 4;6;7 | null | null | Training Autoencoders by Alternating Minimization | null | null | 0 | 4.333333 | Reject | 4;4;5 | null |
null | UC Berkeley, Department of Electrical Engineering and Computer Science | 2018 | 0 | null | null | 0 | null | null | null | null | null | Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel | https://iclr.cc/virtual/2018/poster/64 | meta-learning;few-shot learning | null | 0 | null | null | iclr | 1 | 0 | null | main | 6.333333 | 6;6;7 | null | null | A Simple Neural Attentive Meta-Learner | null | null | 0 | 3.333333 | Poster | 3;3;4 | null |
null | Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein | https://iclr.cc/virtual/2018/poster/91 | Gaussian process;Bayesian regression;deep networks;kernel methods | null | 0 | null | null | iclr | -0.755929 | 0 | null | main | 5.666667 | 4;6;7 | null | null | Deep Neural Networks as Gaussian Processes | null | null | 0 | 3.666667 | Poster | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep Reinforcement Learning;mult-agent systems | null | 0 | null | null | iclr | 0 | 0 | null | main | 3.333333 | 3;3;4 | null | null | Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning | null | null | 0 | 4 | Reject | 5;3;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 | 5.666667 | 5;5;7 | null | null | Federated Learning: Strategies for Improving Communication Efficiency | null | null | 0 | 4.333333 | Reject | 3;5;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | SVM;siamese network;one-shot learning;few-shot learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 4 | 3;4;5 | null | null | Make SVM great again with Siamese kernel for few-shot learning | null | null | 0 | 4.333333 | Reject | 4;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | autonomous lane changing;decision making;deep reinforcement learning;q-learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 3 | 3;3;3 | null | null | Tactical Decision Making for Lane Changing with Deep Reinforcement Learning | null | null | 0 | 4.666667 | Withdraw | 5;4;5 | null |
null | The University of Tokyo, RIKEN; The University of Tokyo | 2018 | 0 | null | null | 0 | null | null | null | null | null | Yuji Tokozume, Yoshitaka Ushiku, Tatsuya Harada | https://iclr.cc/virtual/2018/poster/259 | sound recognition;supervised learning;feature learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 4;8;9 | null | null | Learning from Between-class Examples for Deep Sound Recognition | https://github.com/mil-tokyo/bc_learning_sound/ | null | 0 | 4 | Poster | 4;4;4 | null |
null | Redwood Center for Theoretical Neuroscience, University of California, Berkeley | 2018 | 0 | null | null | 0 | null | null | null | null | null | Alexander Anderson, Cory P Berg | https://iclr.cc/virtual/2018/poster/244 | Binary Neural Networks;Neural Network Visualization | null | 0 | null | null | iclr | 1 | 0 | null | main | 6 | 4;7;7 | null | null | The High-Dimensional Geometry of Binary Neural Networks | null | null | 0 | 3.666667 | Poster | 3;4;4 | null |
null | Department of Applied Mathematics and Statistics, Johns Hopkins University; Department of Computer Science, Johns Hopkins University | 2018 | 0 | null | null | 0 | null | null | null | null | null | Raman Arora, Amitabh Basu, Poorya Mianjy, Anirbit Mukherjee | https://iclr.cc/virtual/2018/poster/155 | expressive power;benefits of depth;empirical risk minimization;global optimality;computational hardness;combinatorial optimization | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 6.333333 | 6;6;7 | null | null | Understanding Deep Neural Networks with Rectified Linear Units | null | null | 0 | 4.333333 | Poster | 4;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep Reinforcement Learning;Multi-Agent Reinforcement Learning;StarCraft Micromanagement Tasks | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Revisiting The Master-Slave Architecture In Multi-Agent Deep Reinforcement Learning | null | null | 0 | 4 | Reject | 3;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep learning;Structured Prediction;Natural Language Processing;Neural Program Synthesis | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.333333 | 4;5;7 | null | null | Neural Program Search: Solving Data Processing Tasks from Description and Examples | null | null | 0 | 4 | Workshop | 4;4;4 | null |
null | Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Abram Friesen, Pedro Domingos | https://iclr.cc/virtual/2018/poster/92 | hard-threshold units;combinatorial optimization;target propagation;straight-through estimation;quantization | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 7;7;7 | null | null | Deep Learning as a Mixed Convex-Combinatorial Optimization Problem | null | null | 0 | 3.666667 | Poster | 3;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | domain adaptation;neural networks;generative models;discriminative models | null | 0 | null | null | iclr | -1 | 0 | null | main | 5.333333 | 5;5;6 | null | null | Principled Hybrids of Generative and Discriminative Domain Adaptation | null | null | 0 | 3.666667 | Reject | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | style transfer;text generation;non-parallel data | null | 0 | null | null | iclr | 0 | 0 | null | main | 0 | null | null | null | Language Style Transfer from Non-Parallel Text with Arbitrary Styles | null | null | 0 | 0 | Withdraw | null | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep reinforcement learning;task execution;instruction execution | null | 0 | null | null | iclr | -0.5 | 0 | https://youtu.be/e_ZXVS5VutM | main | 5.333333 | 4;6;6 | null | null | Neural Task Graph Execution | null | null | 0 | 3.666667 | Reject | 4;3;4 | null |
null | Preferred Networks, Inc.; Ritsumeikan University; National Institute of Informatics | 2018 | 0 | null | null | 0 | null | null | null | null | null | Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida | https://iclr.cc/virtual/2018/poster/331 | Generative Adversarial Networks;Deep Generative Models;Unsupervised Learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 7.333333 | 7;7;8 | null | null | Spectral Normalization for Generative Adversarial Networks | https://github.com/pfnet-research/sngan_projection | null | 0 | 3 | Oral | 4;2;3 | null |
null | Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583 | 2018 | 0 | null | null | 0 | null | null | null | null | null | Pan Zhou, Jiashi Feng, Pan Zhou | https://iclr.cc/virtual/2018/poster/329 | Deep Learning Analysis;Deep Learning Theory;Empirical Risk;Landscape Analysis;Nonconvex Optimization | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.666667 | 3;7;7 | null | null | Empirical Risk Landscape Analysis for Understanding Deep Neural Networks | null | null | 0 | 3 | Poster | 3;3;3 | null |
null | MPI for Intelligent Systems; University of Amsterdam; Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schoelkopf | https://iclr.cc/virtual/2018/poster/76 | fidelity-weighted learning;semisupervised learning;weakly-labeled data;teacher-student | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 5;6;7 | null | null | Fidelity-Weighted Learning | null | null | 0 | 3.666667 | Poster | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Distributional shift;causal effects;domain adaptation | null | 0 | null | null | iclr | 0.188982 | 0 | null | main | 6.666667 | 5;7;8 | null | null | Learning Weighted Representations for Generalization Across Designs | null | null | 0 | 3.333333 | Reject | 3;4;3 | null |
null | Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | Samuel Smith, Pieter-Jan Kindermans, Chris Ying, Quoc V Le | https://iclr.cc/virtual/2018/poster/272 | batch size;learning rate;simulated annealing;large batch training;scaling rules;stochastic gradient descent;sgd;imagenet;optimization | null | 0 | null | null | iclr | 0 | 0 | null | main | 6.333333 | 6;6;7 | null | null | Don't Decay the Learning Rate, Increase the Batch Size | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Bayesian Deep Learning;Amortized Inference;Variational Auto-Encoders;Learning to Learn | null | 0 | null | null | iclr | 1 | 0 | null | main | 5.333333 | 5;5;6 | null | null | Learning to Infer | null | null | 0 | 4.333333 | Workshop | 4;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | GAN;medical;records;time;series;generation;privacy | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 4;5;6 | null | null | Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | Asit Mishra, Eriko Nurvitadhi, Jeffrey J Cook, Debbie Marr | https://iclr.cc/virtual/2018/poster/208 | Low precision;binary;ternary;4-bits networks | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 6.333333 | 5;5;9 | null | null | WRPN: Wide Reduced-Precision Networks | null | null | 0 | 3.666667 | Poster | 4;3;4 | null |
null | Accelerator Architecture Lab, Intel Labs | 2018 | 0 | null | null | 0 | null | null | null | null | null | Asit Mishra, Debbie Marr | https://iclr.cc/virtual/2018/poster/173 | Ternary;4-bits;low precision;knowledge distillation;knowledge transfer;model compression | null | 0 | null | null | iclr | 0 | 0 | null | main | 7.333333 | 7;7;8 | null | null | Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | Xu He, Herbert Jaeger | https://iclr.cc/virtual/2018/poster/233 | Catastrophic Interference;Conceptor;Backpropagation;Continual Learning;Lifelong Learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 7;7;7 | null | null | Overcoming Catastrophic Interference using Conceptor-Aided Backpropagation | null | null | 0 | 3.666667 | Poster | 3;3;5 | null |
null | Paper under double-blind review | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Reading Comprehension;Answering Multiple Choice Questions | null | 0 | null | null | iclr | -1 | 0 | null | main | 4.666667 | 4;5;5 | null | null | ElimiNet: A Model for Eliminating Options for Reading Comprehension with Multiple Choice Questions | null | null | 0 | 3.333333 | Reject | 4;3;3 | null |
null | Princeton University; Columbia University | 2018 | 0 | null | null | 0 | null | null | null | null | null | Sanjeev Arora, Mikhail Khodak, Nikunj Umesh Saunshi, Kiran Vodrahalli | https://iclr.cc/virtual/2018/poster/96 | theory;LSTM;unsupervised learning;word embeddings;compressed sensing;sparse recovery;document representation;text classification | null | 0 | null | null | iclr | -0.755929 | 0 | null | main | 6.666667 | 6;7;7 | null | null | A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs | null | null | 0 | 2.666667 | Poster | 4;3;1 | null |
null | The University of Melbourne, Parkville, Australia; National Institute of Informatics, Tokyo, Japan; University of Michigan, Ann Arbor, USA; Tsinghua University, Beijing, China; University of California, Berkeley, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Xingjun Ma, Bo Li, Yisen Wang, Sarah Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E Houle, James Bailey | https://iclr.cc/virtual/2018/poster/328 | Adversarial Subspace;Local Intrinsic Dimensionality;Deep Neural Networks | null | 0 | null | null | iclr | 0.654654 | 0 | null | main | 7 | 6;7;8 | null | null | Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality | null | null | 0 | 2.666667 | Oral | 1;4;3 | null |
null | Department of Computer Science, University of Bonn, Germany; Fraunhofer Institute IAIS, Sankt Augustin, Germany | 2018 | 0 | null | null | 0 | null | null | null | null | null | Henning Petzka, Asja Fischer, Denis Lukovnikov | https://iclr.cc/virtual/2018/poster/17 | null | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 5 | 2;6;7 | null | null | On the regularization of Wasserstein GANs | null | null | 0 | 3.666667 | Poster | 2;5;4 | null |
null | Robotics Institute, Carnegie Mellon University; Volvo Construction Equipment, Volvo Group | 2018 | 0 | null | null | 0 | null | null | null | null | null | Anubhav Ashok, Nicholas Rhinehart, Fares Beainy, Kris M Kitani | https://iclr.cc/virtual/2018/poster/132 | Deep learning;Neural networks;Model compression | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 4;5;9 | null | null | N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | inversion scheme;deep neural networks;semi-supervised learning;MNIST;SVHN;CIFAR10 | null | 0 | null | null | iclr | -1 | 0 | null | main | 5.333333 | 4;5;7 | null | null | Semi-Supervised Learning via New Deep Network Inversion | null | null | 0 | 3.666667 | Reject | 5;4;2 | null |
null | Courant Institute of Mathematical Sciences, Center for Data Science, New York, NY 10012, USA; Courant Institute of Mathematical Sciences, Center for Data Science, New York University, New York, NY 10012, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Alex Nowak, David Folqué Garcia, Joan Bruna | https://iclr.cc/virtual/2018/poster/44 | Neural Networks;Combinatorial Optimization;Algorithms | null | 0 | null | null | iclr | 0 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Divide and Conquer Networks | null | null | 0 | 3 | Poster | 3;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Word Embeddings;Tensor Factorization;Natural Language Processing | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 5;5;5 | null | null | LEARNING SEMANTIC WORD RESPRESENTATIONS VIA TENSOR FACTORIZATION | null | null | 0 | 4.333333 | Reject | 3;5;5 | null |
null | †The University of Hong Kong; ‡Salesforce Research | 2018 | 0 | null | null | 0 | null | null | null | null | null | Jiatao Gu, James Bradbury, Caiming Xiong, Victor OK Li, richard socher | https://iclr.cc/virtual/2018/poster/241 | machine translation;non-autoregressive;transformer;fertility;nmt | null | 0 | null | null | iclr | 0 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Non-Autoregressive Neural Machine Translation | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | UC Irvine; Amazon.com; Snap Inc; UC Santa Barbara; Think Big Analytics | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Recommender systems;deep learning;personalization | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 6.333333 | 6;6;7 | null | null | THE EFFECTIVENESS OF A TWO-LAYER NEURAL NETWORK FOR RECOMMENDATIONS | null | null | 0 | 3.333333 | Workshop | 3;4;3 | null |
null | N/A | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | vocabulary-informed learning;data augmentation | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 5 | 4;5;6 | null | null | VOCABULARY-INFORMED VISUAL FEATURE AUGMENTATION FOR ONE-SHOT 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 | structured prediction;RAML;theory;Bayes decision rule;reward function | null | 0 | null | null | iclr | 0 | 0 | null | main | 5.333333 | 5;5;6 | null | null | Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML | null | null | 0 | 3 | Reject | 4;2;3 | null |
null | The Institute for Theoretical Computer Science, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China; Department of Computer Science, University of Southern California, Los Angeles, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Jiayuan Mao, Honghua Dong, Joseph J Lim | https://iclr.cc/virtual/2018/poster/3 | reinforcement learning;transfer learning | null | 0 | null | null | iclr | 1 | 0 | null | main | 6.333333 | 6;6;7 | null | null | Universal Agent for Disentangling Environments and Tasks | null | null | 0 | 3.333333 | Poster | 3;3;4 | null |
null | Stanford University; Google AI Perception; Google Brain | 2018 | 0 | null | null | 0 | null | null | null | null | null | Daniel Levy, Matthew D Hoffman, Jascha Sohl-Dickstein | https://iclr.cc/virtual/2018/poster/284 | markov;chain;monte;carlo;sampling;posterior;deep;learning;hamiltonian;mcmc | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 7 | 6;7;8 | null | null | Generalizing Hamiltonian Monte Carlo with Neural Networks | https://github.com/google-research/google-research/tree/master/generalizing_hmc | null | 0 | 3 | Poster | 3;4;2 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Reinforcement learning | null | 0 | null | null | iclr | 1 | 0 | null | main | 5.666667 | 5;6;6 | null | null | Learning Gaussian Policies from Smoothed Action Value Functions | null | null | 0 | 3.666667 | Reject | 3;4;4 | null |
null | Department of Computer Science and Engineering, Indian Institute of Technology, Madras; Department of Mechanical Engineering, Indian Institute of Technology, Madras; Department of Electrical Engineering, Indian Institute of Technology, Madras; Department of Computer Science and Engineering, and Robert Bosch Centre for Data Science and AI (RBC-DSAI), Indian Institute of Technology, Madras | 2018 | 0 | null | null | 0 | null | null | null | null | null | Sahil Sharma, Ashutosh Kumar Jha, Parikshit Hegde, Balaraman Ravindran | https://iclr.cc/virtual/2018/poster/257 | Deep Reinforcement Learning | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 6.333333 | 5;7;7 | null | null | Learning to Multi-Task by Active Sampling | null | null | 0 | 3.666667 | Poster | 3;3;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | forward modeling;partially observable;deep learning;strategy game;real-time strategy | null | 0 | null | null | iclr | 0.944911 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger | null | null | 0 | 2.666667 | Reject | 1;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | label noise;weakly supervised learning;robustness of neural networks;deep learning;large datasets | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Deep Learning is Robust to Massive Label Noise | null | null | 0 | 4.666667 | Reject | 5;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep Learning;Deconvolutional Layer;Pixel CNN | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 5.333333 | 5;5;6 | null | null | Pixel Deconvolutional 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 | Word embedding;tensor decomposition | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 5;5;5 | null | null | Learning Covariate-Specific Embeddings with Tensor Decompositions | null | null | 0 | 4 | Reject | 4;3;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | LVCSR;speech recognition;embedded;low rank factorization;RNN;GRU;trace norm | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Trace norm regularization and faster inference for embedded speech recognition RNNs | https://github.com/paddlepaddle/farm | null | 0 | 3.666667 | Reject | 3;3;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | adversarial attacks;security;auto-encoder | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 4 | 3;4;5 | null | null | LatentPoison -- Adversarial Attacks On The Latent Space | null | null | 0 | 3.666667 | Reject | 4;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | natural language processing;background knowledge;word embeddings;question answering;natural language inference | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 5.333333 | 5;5;6 | null | null | Dynamic Integration of Background Knowledge in Neural NLU Systems | null | null | 0 | 3.666667 | Reject | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Manifold Learning;Non-linear Dimensionality Reduction;Neural Networks;Unsupervised Learning | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 4 | 3;4;5 | null | null | Parametric Manifold Learning Via Sparse Multidimensional Scaling | null | null | 0 | 4.333333 | Reject | 4;4;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 | Attribute-aware Collaborative Filtering: Survey and Classification | null | null | 0 | 4.666667 | Withdraw | 5;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | price predictions;expert system;recurrent neural networks;deep learning;natural language processing | null | 0 | null | null | iclr | 1 | 0 | null | main | 4.666667 | 4;4;6 | null | null | Predicting Auction Price of Vehicle License Plate with Deep Recurrent Neural Network | null | null | 0 | 4.333333 | Reject | 4;4;5 | null |
null | Universit´e de Bretagne Sud, IRISA, UMR 6074, CNRS; Kyoto University, Graduate School of Informatics; NTT Communication Science Laboratories; Universit´e Cˆote d'Azur, Lagrange, UMR 7293, CNRS, OCA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel | https://iclr.cc/virtual/2018/poster/179 | optimal transport;Wasserstein;domain adaptation;generative models;Monge map;optimal mapping | null | 0 | null | null | iclr | 0 | 0 | null | main | 6.75 | 6;6;7;8 | null | null | Large Scale Optimal Transport and Mapping Estimation | null | null | 0 | 3 | Poster | 3;3;3;3 | null |
null | Google | 2018 | 0 | null | null | 0 | null | null | null | null | null | H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang | https://iclr.cc/virtual/2018/poster/187 | differential privacy;LSTMs;language models;privacy | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 7.333333 | 7;7;8 | null | null | Learning Differentially Private Recurrent Language Models | null | null | 0 | 3.333333 | Poster | 2;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 | 4.666667 | 4;5;5 | null | null | Learning what to learn in a neural program | null | null | 0 | 3.333333 | Reject | 4;4;2 | 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 | Multimodal Sentiment Analysis To Explore the Structure of Emotions | null | null | 0 | 5 | Reject | 5;5;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | RNNs | null | 0 | null | null | iclr | -0.188982 | 0 | null | main | 4.333333 | 3;4;6 | null | null | Efficiently applying attention to sequential data with the Recurrent Discounted Attention unit | null | null | 0 | 4.333333 | Reject | 4;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Reinforcement Learning;Imitation Learning | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 5.666667 | 5;6;6 | null | null | Faster Reinforcement Learning with Expert State Sequences | null | null | 0 | 4 | Reject | 5;4;3 | null |
null | Department of Engineering Science, University of Oxford; Department of Statistics, University of Oxford | 2018 | 0 | null | null | 0 | null | null | null | null | null | Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood | https://iclr.cc/virtual/2018/poster/31 | Variational Autoencoders;Inference amortization;Model learning;Sequential Monte Carlo;ELBOs | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 5.666667 | 3;7;7 | null | null | Auto-Encoding Sequential Monte Carlo | null | null | 0 | 3 | Poster | 2;4;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | supervised learning;unsupervised learning;self-organization;internal representation;topological structure | null | 0 | null | null | iclr | -1 | 0 | null | main | 2.666667 | 2;2;4 | null | null | Self-Organization adds application robustness to deep learners | null | null | 0 | 4.666667 | Withdraw | 5;5;4 | null |
null | Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology; Paul G. Allen School of Computer Science & Engineering, University of Washington | 2018 | 0 | null | null | 0 | null | null | null | null | null | Yonatan Belinkov, Yonatan Bisk | https://iclr.cc/virtual/2018/poster/172 | neural machine translation;characters;noise;adversarial examples;robust training | null | 0 | null | null | iclr | 0 | 0 | null | main | 7.333333 | 7;7;8 | null | null | Synthetic and Natural Noise Both Break Neural Machine Translation | null | null | 0 | 4 | Oral | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Deep Reinforcement Learning;Domain Adaptation;Adversarial Networks | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 3 | 2;3;4 | null | null | Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games | null | null | 0 | 4 | Reject | 4;3;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | linear quadratic regulator;policy gradient;natural gradient;reinforcement learning;non-convex optimization | null | 0 | null | null | iclr | 1 | 0 | null | main | 5.333333 | 5;5;6 | null | null | Global Convergence of Policy Gradient Methods for Linearized Control Problems | null | null | 0 | 3.333333 | Reject | 3;3;4 | null |
null | Under double-blind review | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | adversarial examples | null | 0 | null | null | iclr | -0.944911 | 0 | https://youtu.be/YXy6oX1iNoA | main | 6.333333 | 5;6;8 | null | null | Synthesizing Robust Adversarial Examples | null | null | 0 | 3.666667 | Reject | 4;4;3 | null |
null | Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | Murat Kocaoglu, Christopher Snyder, Alexandros Dimakis, Sriram Vishwanath | https://iclr.cc/virtual/2018/poster/159 | causality;structural causal models;GANs;conditional GANs;BEGAN;adversarial training | null | 0 | null | null | iclr | 0 | 0 | null | main | 7.333333 | 6;7;9 | null | null | CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training | null | null | 0 | 3 | Poster | 3;3;3 | null |
null | Racah Institute of Physics, The Hebrew University of Jerusalem; Department of Engineering Science, University of Oxford; Assistant Professor at the Federal University of Rio Grande, Rio Grande, Brazil | 2018 | 0 | null | null | 0 | null | null | null | null | null | Zohar Ringel, Rodrigo Andrade de Bem | https://iclr.cc/virtual/2018/poster/303 | Deep Convolutional Networks;Loss function landscape;Graph Structured Data;Training Complexity;Theory of deep learning;Percolation theory;Anderson Localization | null | 0 | null | null | iclr | 1 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Critical Percolation as a Framework to Analyze the Training of Deep Networks | null | null | 0 | 2.333333 | Poster | 1;3;3 | null |
null | Microsoft Business AI and Research, National Taiwan University; Microsoft Business AI and Research | 2018 | 0 | null | null | 0 | null | null | null | null | null | Hsin-Yuan Huang, Chenguang Zhu, Yelong Shen, Weizhu Chen | https://iclr.cc/virtual/2018/poster/246 | Attention Mechanism;Machine Comprehension;Natural Language Processing;Deep Learning | null | 0 | null | null | iclr | -0.866025 | 0 | null | main | 7.333333 | 7;7;8 | null | null | FusionNet: Fusing via Fully-aware Attention with Application to Machine Comprehension | null | null | 0 | 4 | Poster | 5;4;3 | null |
null | Department of Computer Science and Operations Research, University of Montréal, Canada; Department of Computer Science, Stanford University, USA | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | representation learning;auto-encoders;3D point clouds;generative models;GANs;Gaussian Mixture Models | null | 0 | null | null | iclr | 0.755929 | 0 | null | main | 6.333333 | 5;6;8 | null | null | Learning Representations and Generative Models for 3D Point Clouds | null | null | 0 | 4.666667 | Workshop | 4;5;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | VAE;Generative Model;Vision;Natural Language | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 4.666667 | 4;5;5 | null | null | Generative Entity Networks: Disentangling Entitites and Attributes in Visual Scenes using Partial Natural Language Descriptions | null | null | 0 | 4.333333 | Reject | 4;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | learning from demonstration;reinforcement learning;maximum entropy learning | null | 0 | null | null | iclr | 0.866025 | 0 | null | main | 5.333333 | 5;5;6 | null | null | Reinforcement Learning from Imperfect Demonstrations | null | null | 0 | 4 | Workshop | 3;4;5 | null |
null | Washington State University, Pullman; NEC Laboratories America | 2018 | 0 | null | null | 0 | null | null | null | null | null | Bo Zong, Qi Song, Martin Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, Haifeng Chen | https://iclr.cc/virtual/2018/poster/126 | Density estimation;unsupervised anomaly detection;high-dimensional data;Deep autoencoder;Gaussian mixture modeling;latent low-dimensional space | null | 0 | null | null | iclr | 0 | 0 | null | main | 8 | 8;8;8 | null | null | Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection | null | null | 0 | 4.333333 | Poster | 5;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | deep neural networks;short text classification;cybersecurity;domain generation algorithms;malicious domain names | null | 0 | null | null | iclr | 0.188982 | 0 | null | main | 5.333333 | 4;5;7 | null | null | Character Level Based Detection of DGA Domain Names | null | null | 0 | 3.666667 | Reject | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | hierarchical;tree-lstm;treelstm;syntax;composition | null | 0 | null | null | iclr | 0 | 0 | null | main | 5 | 4;5;6 | null | null | Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs | null | null | 0 | 4 | Reject | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | Xu Chen, Jiang Wang, Hao Ge | https://iclr.cc/virtual/2018/poster/273 | GAN;Primal-Dual Subgradient;Mode Collapse;Saddle Point | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 6.666667 | 6;7;7 | null | null | TRAINING GENERATIVE ADVERSARIAL NETWORKS VIA PRIMAL-DUAL SUBGRADIENT METHODS: A LAGRANGIAN PERSPECTIVE ON GAN | null | null | 0 | 3.666667 | Poster | 4;3;4 | null |
null | Simon Fraser University, Burnaby, BC, Canada; Microsoft Research, Cambridge, UK | 2018 | 0 | null | null | 0 | null | null | null | null | null | Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi | https://iclr.cc/virtual/2018/poster/216 | programs;source code;graph neural networks | null | 0 | null | null | iclr | 0 | 0 | null | main | 8 | 8;8;8 | null | null | Learning to Represent Programs with Graphs | null | null | 0 | 4 | Oral | 4;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | optimization;vanishing gradients;shattered gradients;skip-connections | null | 0 | null | null | iclr | 0 | 0 | null | main | 6 | 6;6;6 | null | null | Avoiding degradation in deep feed-forward networks by phasing out skip-connections | null | null | 0 | 4.333333 | Reject | 4;5;4 | null |
null | Carnegie Mellon University; DeepMind | 2018 | 0 | null | null | 0 | null | null | null | null | null | Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, Koray Kavukcuoglu | https://iclr.cc/virtual/2018/poster/45 | deep learning;architecture search | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 6.666667 | 6;6;8 | null | null | Hierarchical Representations for Efficient Architecture Search | null | null | 0 | 3.666667 | Poster | 3;4;4 | null |
null | The University of Tokyo | 2018 | 0 | null | null | 0 | null | null | null | null | null | Raphael Shu, Hideki Nakayama | https://iclr.cc/virtual/2018/poster/242 | natural language processing;word embedding;compression;deep learning | null | 0 | null | null | iclr | 0 | 0 | null | main | 7 | 6;7;8 | null | null | Compressing Word Embeddings via Deep Compositional Code Learning | null | null | 0 | 4 | Poster | 4;4;4 | null |
null | null | 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;4;6 | null | null | The Context-Aware Learner | null | null | 0 | 4 | Reject | 4;3;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Few-Shot Learning;Neural Network Understanding;Visual Concepts | null | 0 | null | null | iclr | -0.755929 | 0 | null | main | 5.333333 | 4;5;7 | null | null | Unleashing the Potential of CNNs for Interpretable Few-Shot Learning | null | null | 0 | 4.333333 | Reject | 5;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L Griffiths | https://iclr.cc/virtual/2018/poster/313 | meta-learning;learning to learn;hierarchical Bayes;approximate Bayesian methods | null | 0 | null | null | iclr | 0 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Recasting Gradient-Based Meta-Learning as Hierarchical Bayes | null | null | 0 | 3 | Poster | 3;3;3 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | Guillaume Bellec, David Kappel, Wolfgang Maass, Robert Legenstein | https://iclr.cc/virtual/2018/poster/291 | deep learning;pruning;LSTM;convolutional networks;recurrent neural network;sparse networks;neuromorphic hardware;energy efficient computing;low memory hardware;stochastic differential equation;fokker-planck equation | null | 0 | null | null | iclr | -0.755929 | 0 | null | main | 6.333333 | 5;6;8 | null | null | Deep Rewiring: Training very sparse deep networks | null | null | 0 | 4.333333 | Poster | 5;4;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | 3D fMRI data;Deep Learning;Generative Adversarial Network;Classification | null | 0 | null | null | iclr | 0.981981 | 0 | null | main | 6.333333 | 5;6;8 | null | null | Hallucinating brains with artificial brains | null | null | 0 | 4 | Reject | 3;4;5 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | graph topology;GAN;network science;hierarchical learning | null | 0 | null | null | iclr | 0.5 | 0 | null | main | 3.666667 | 3;4;4 | null | null | Graph Topological Features via GAN | null | null | 0 | 4.333333 | Reject | 4;5;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | Sanjeev Arora, Andrej Risteski, Yi Zhang | https://iclr.cc/virtual/2018/poster/72 | Generative Adversarial Networks;mode collapse;birthday paradox;support size estimation | null | 0 | null | null | iclr | -0.5 | 0 | null | main | 6.666667 | 6;7;7 | null | null | Do GANs learn the distribution? Some Theory and Empirics | null | null | 0 | 3.666667 | Poster | 4;3;4 | null |
null | null | 2018 | 0 | null | null | 0 | null | null | null | null | null | null | null | Machine learning;Neural networks;Sparse neural networks;Pre-defined sparsity;Scatter;Connectivity patterns;Adjacency matrix;Parameter Reduction;Morse code | null | 0 | null | null | iclr | 0 | 0 | null | main | 4.333333 | 4;4;5 | null | null | Characterizing Sparse Connectivity Patterns in Neural Networks | null | null | 0 | 3 | Reject | 3;3;3 | null |