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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
; Google
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Scott Reed, Yutian Chen, Thomas Paine, Aaron v den, S. M. Ali Eslami, Danilo Jimenez Rezende, Oriol Vinyals, Nando de Freitas
|
https://iclr.cc/virtual/2018/poster/189
|
few-shot learning;density models;meta learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Learnability;Generalizability;Understanding Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Learnability of Learned 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 |
deep learning;sparsity;adaptive methods
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Adaptive Weight Sparsity for Training Deep Neural Networks
| null | null | 0 | 3 |
Withdraw
|
4;2;3
| null |
null |
School of Computer Science, The Hebrew University, Israel; The Blavatnik School of Computer Science, Tel Aviv University, Israel
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz
|
https://iclr.cc/virtual/2018/poster/254
|
Deep Learning;Non-convex Optmization;Generalization;Learning Theory;Neural Networks
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Imitation Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Deterministic Policy Imitation Gradient Algorithm
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
large batch;LARS;adaptive rate scaling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Large Batch Training of Convolutional Networks with Layer-wise Adaptive Rate Scaling
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hypergraph;representation learning;tensors
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Hyperedge2vec: Distributed Representations for Hyperedges
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Markov chain;discovering orders;generative model;one-shot
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Discovering Order in Unordered Datasets: Generative Markov Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
speech;generation;accent;gan;adversarial;reinforcement;memory;lstm;policy;gradients;human
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
POLICY DRIVEN GENERATIVE ADVERSARIAL NETWORKS FOR ACCENTED SPEECH GENERATION
| null | null | 0 | 4 |
Reject
|
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 | 0 | null |
main
| 3.666667 |
2;4;5
| null | null |
Generative Models for Alignment and Data Efficiency in Language
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
next utterance selection;ubuntu dialogue corpus;out-of-vocabulary;word representation
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Enhance Word Representation for Out-of-Vocabulary on Ubuntu Dialogue Corpus
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
TTI-Chicago
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
co-training;phonetics;unsupervised learning;mutual information
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Information Theoretic Co-Training
| null | null | 0 | 3.666667 |
Withdraw
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
natural language processing;word embeddings;language models;neural network;deep learning;sparsity;dropout
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
A Simple Fully Connected Network for Composing Word Embeddings from Characters
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
recurrent neural networks;natural language processing
| null | 0 | null | null |
iclr
| -0.27735 | 0 | null |
main
| 6.333333 |
4;7;8
| null | null |
Training RNNs as Fast as CNNs
| 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;tree transduction
| null | 0 | null | null |
iclr
| -0.654654 | 0 | null |
main
| 4 |
2;3;7
| null | null |
Neural Tree Transducers for Tree to Tree 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 |
distill;transfer;classification;alignment;verification
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
mean field;dynamics;residual network;variance variation;width variation;initialization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Deep Mean Field Theory: Layerwise Variance and Width Variation as Methods to Control Gradient Explosion
| null | null | 0 | 2.333333 |
Workshop
|
1;3;3
| null |
null |
Facebook AI Research; Center for Data Science. New York University; Department of Computer Science. New York University; CIFAR Azrieli Global Scholar
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Katrina Evtimova, Andrew Drozdov, Douwe Kiela, Kyunghyun Cho
|
https://iclr.cc/virtual/2018/poster/212
|
emergent communication;multi-agent systems;multi-modal
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Emergent Communication in a Multi-Modal, Multi-Step Referential Game
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural networks;generative adversarial networks;parallel
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
NOVEL AND EFFECTIVE PARALLEL MIX-GENERATOR GENERATIVE ADVERSARIAL 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 |
reinforcement learning;cooperation;social dilemmas;game theory
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Maintaining cooperation in complex social dilemmas using deep 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 |
reinforcement learning;hierarchy;options;inference
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
An inference-based policy gradient method for learning options
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Under double-blind review
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
dataset;human-designed;language understanding
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;7
| null | null |
Large-scale Cloze Test Dataset Designed by Teachers
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
manifold learning;transfer learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Transfer Learning on Manifolds via Learned Transport Operators
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Time Series Forecasting;Change Point Detection;Anomaly Detection;State Space Model;Bayesian
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Bayesian Time Series Forecasting with Change Point and Anomaly Detection
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null |
Columbia University, Google; University of Toronto, Vector Institute
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse
|
https://iclr.cc/virtual/2018/poster/35
|
weight perturbation;reparameterization gradient;gradient variance reduction;evolution strategies;LSTM;regularization;optimization
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
Laboratoire de Recherche en Informatique, Université Paris Sud, Gif-sur-Yvette, 91190, France
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Corentin Tallec, Yann Ollivier
|
https://iclr.cc/virtual/2018/poster/184
|
RNN
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Unbiased Online Recurrent Optimization
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graphs;Structural Similarities;Spectral Graph Wavelets;Graph Signal Processing;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Spectral Graph Wavelets for Structural Role Similarity in Networks
| 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;computer vision;generative adversarial networks
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Learning Priors for Adversarial Autoencoders
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Models;Hierarchical Models;Latent Variable Models
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Learning Generative Models with Locally Disentangled Latent Factors
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Microsoft Research India; University of Washington Seattle
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M Kakade
|
https://iclr.cc/virtual/2018/poster/46
|
Stochastic Gradient Descent;Deep Learning;Momentum;Acceleration;Heavy Ball;Nesterov Acceleration;Stochastic Optimization;SGD;Accelerated Stochastic Gradient Descent
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
On the insufficiency of existing momentum schemes for Stochastic Optimization
|
https://github.com/rahulkidambi/AccSGD
| null | 0 | 4 |
Oral
|
3;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Lifelong learning;Transfer learning;PAC-Bayes theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Lifelong Learning by Adjusting Priors
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Stanford University; Université de Montréal; Microsoft Research
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman
|
https://iclr.cc/virtual/2018/poster/163
|
Adversarial Examples;Generative Models;Purification;Hypothesis Testing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
recurrent neural network;language modeling;dense connection
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
2;4;4
| null | null |
Dense Recurrent Neural Network with Attention Gate
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Pattern Analysis and Computer Vision (PA VIS), Istituto Italiano di Tecnologia - Genova, Italy; University of Verona, Department of Computer Science - Verona, Italy
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Pietro Morerio, Jacopo Cavazza, Vittorio Murino
|
https://iclr.cc/virtual/2018/poster/223
|
unsupervised domain adaptation;entropy minimization;image classification;deep transfer learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation
| null | null | 0 | 4.666667 |
Poster
|
5;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Networks;Latent Space Modeling
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Autoregressive Generative Adversarial Networks
| null | null | 0 | 4.666667 |
Workshop
|
5;4;5
| null |
null |
Montréal Institute for Learning Algorithms; Centre de Visió per Computador, UAB; Department of Computer Science and Technology, University of Cambridge; Montréal Institute for Learning Algorithms, Facebook AI Research
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Petar Veličković, Guillem Cucurull Preixens, Arantxa Casanova Paga, Adriana Romero, Pietro Liò, Yoshua Bengio
|
https://iclr.cc/virtual/2018/poster/299
|
Deep Learning;Graph Convolutions;Attention;Self-Attention
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Graph Attention Networks
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
School of Science, Technology, Engineering & Mathematics, University of Washington - Bothell; Paul G. Allen School of Computer Science & Engineering, University of Washington
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Antoine Bosselut, Omer Levy, Ariel Holtzman, Corin Ennis, Dieter Fox, Yejin Choi
|
https://iclr.cc/virtual/2018/poster/85
|
representation learning;memory networks;state tracking
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.666667 |
6;8;9
| null | null |
Simulating Action Dynamics with Neural Process Networks
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
filter generation;meta-learning;filter repository;image classification;dynamic generation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Learning to Generate Filters for Convolutional 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 |
neural networks;convolutional neural networks;time series;asynchronous data;regression
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Autoregressive Convolutional Neural Networks for Asynchronous Time Series
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Skip connection;generalization;gegularization;deep network;representation.
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 2.666667 |
2;3;3
| null | null |
On the Generalization Effects of DenseNet Model Structures
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
computer vision;deep learning;convolutional neural networks;attention
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
A Painless Attention Mechanism for 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 |
One-shot learning;few-shot learning;deep learning;simplex
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Few-Shot Learning with Simplex
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Neural Network;Attention;Semi-supervised Learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Attention-based Graph Neural Network for Semi-supervised Learning
| null | null | 0 | 3 |
Reject
|
2;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
interpretable classification;decision trees;deep learning;variational autoencoder
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Interpretable Classification via Supervised Variational Autoencoders and Differentiable Decision Trees
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
recurrent neural network;LSTM;long-short term memory network;machine translation;generalization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Towards Binary-Valued Gates for Robust LSTM Training
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
University of Alberta
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Adversarial Policy Gradient for Alternating Markov Games
| null | null | 0 | 3.333333 |
Workshop
|
4;4;2
| null |
null |
School of Information System, Singapore Management University; AI Foundations - Learning, IBM Research AI
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, Murray Campbell
|
https://iclr.cc/virtual/2018/poster/102
|
Question Answering;Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
| null | null | 0 | 3 |
Poster
|
4;2;3
| null |
null |
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Roy Fox, Richard Shin, Sanjay Krishnan, Ken Goldberg, Dawn Song, Ion Stoica
|
https://iclr.cc/virtual/2018/poster/30
|
Neural programming;Hierarchical Control
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Parametrized Hierarchical Procedures for Neural Programming
| null | null | 0 | 2 |
Poster
|
1;2;3
| null |
null |
School of Cyberspace Security, Beijing University of Posts and Telecommunications, China; ColorfulClouds Technology Co., Ltd, Beijing, China
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Da Xiao, Jo-Yu Liao, Xingyuan Yuan
|
https://iclr.cc/virtual/2018/poster/197
|
neural programming;Neural Programmer-Interpreter
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
3;7;7
| null | null |
Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, Madrid, Spain; PROWLER.io, Cambridge, UK
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Sergio Valcarcel Macua, Javier Zazo, Santiago Zazo
|
https://iclr.cc/virtual/2018/poster/74
|
Stochastic games;potential games;closed loop;reinforcement learning;multiagent systems
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Learning Parametric Closed-Loop Policies for Markov Potential Games
| null | null | 0 | 2 |
Poster
|
3;1;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
SGD;Deep Learning;Generalization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Three factors influencing minima in SGD
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural architecture;inference time reduction;hybrid model
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
HybridNet: A Hybrid Neural Architecture to Speed-up Autoregressive Models
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial examples;Neural Networks;Clipping
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Clipping Free Attacks Against Neural Networks
| null | null | 0 | 2.666667 |
Reject
|
3;3;2
| 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
| 2.666667 |
2;3;3
| null | null |
Lung Tumor Location and Identification with AlexNet and a Custom CNN
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Over-parametrization;Hessian;Eigenvalues;Flat minima;Large batch Small batch
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Empirical Analysis of the Hessian of Over-Parametrized Neural Networks
| null | null | 0 | 3.333333 |
Workshop
|
4;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;deep reinforcement learning;robotics;perception
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
TRL: Discriminative Hints for Scalable Reverse Curriculum Learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative adversarial networks;differential privacy;synthetic data
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Generating Differentially Private Datasets Using GANs
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Google Brain, Mountain View, CA, USA; University of Michigan, Ann Arbor, MI, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Lajanugen Logeswaran, Honglak Lee
|
https://iclr.cc/virtual/2018/poster/129
|
sentence;embeddings;unsupervised;representations;learning;efficient
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
An efficient framework for learning sentence representations
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
University of California Berkeley; University of Washington Seattle
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine
|
https://iclr.cc/virtual/2018/poster/100
|
deep reinforcement learning;reinforcement learning;policy gradients;model-free
| null | 0 | null | null |
iclr
| 0 | 0 |
https://sites.google.com/view/dnc-rl/
|
main
| 6 |
4;7;7
| null | null |
Divide-and-Conquer Reinforcement Learning
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Aleksander Madry, Aleksandar A Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu
|
https://iclr.cc/virtual/2018/poster/67
|
adversarial examples;robust optimization;ML security
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Towards Deep Learning Models Resistant to Adversarial Attacks
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
Department of Computer Science, University of Southern California; DeepMind
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Karol Hausman, Jost Tobias Springenberg, ziyu wang, Nicolas Heess, Martin Riedmiller
|
https://iclr.cc/virtual/2018/poster/6
|
Deep Reinforcement Learning;Variational Inference;Control;Robotics
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Learning an Embedding Space for Transferable Robot Skills
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
End-to-End training;deep neural networks;medical imaging;image reconstruction
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
End-to-End Abnormality Detection in Medical Imaging
| 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 neural networks;geolocation;inception;long-short term memory networks;social media applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
MULTI-MODAL GEOLOCATION ESTIMATION USING DEEP NEURAL NETWORKS
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep reinforcement learning;policy gradient;multidimensional action space;entropy bonus;entropy regularization;discrete action space
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Policy Gradient For Multidimensional Action Spaces: Action Sampling and Entropy Bonus
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Optimal transport;policy gradients;entropy regularization;reinforcement learning;heat equation;Wasserstein;JKO;gradient flows
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Diffusing Policies : Towards Wasserstein Policy Gradient Flows
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5 |
3;4;8
| null | null |
Topic-Based Question Generation
| null | null | 0 | 4 |
Workshop
|
5;4;3
| null |
null |
University of Illinois at Urbana-Champaign; University of California, Berkeley; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H Campbell, Sergey Levine
|
https://iclr.cc/virtual/2018/poster/162
|
video prediction;stochastic prediction;variational inference;unsupervised learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Stochastic Variational Video Prediction
| null | null | 0 | 4.333333 |
Poster
|
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 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Graph Partition Neural Networks for Semi-Supervised Classification
| null | null | 0 | 3 |
Workshop
|
3;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples;universality;neural architecture search
| null | 0 | null | null |
iclr
| -0.39736 | 0 | null |
main
| 5.333333 |
3;5;8
| null | null |
Intriguing Properties of Adversarial Examples
| null | null | 0 | 3 |
Workshop
|
4;2;3
| null |
null |
Bing, Microsoft; Business AI, Microsoft; Electrical and Computer Engineering, Duke University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Wei Wen, Yuxiong He, Samyam Rajbhandari, Minjia Zhang, Wenhan Wang, Fang Liu, Bin Hu, Yiran Chen, Hai Li
|
https://iclr.cc/virtual/2018/poster/337
|
Sparsity;Model Compression;Acceleration;LSTMs;Recurrent Neural Networks;Structural Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Learning Intrinsic Sparse Structures within Long Short-Term Memory
|
https://github.com/wenwei202/iss-rnns
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adam;Adaptive Gradient Methods;weight decay;L2 regularization
| null | 0 | null | null |
iclr
| -0.693375 | 0 | null |
main
| 6.333333 |
4;7;8
| null | null |
Fixing Weight Decay Regularization in Adam
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
spiking neural networks;LSTM;recurrent neural networks
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Gating out sensory noise in a spike-based Long Short-Term Memory network
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Temporal Convolutional Network;Sequence Modeling;Deep Learning
| null | 0 | null | null |
iclr
| 0.693375 | 0 | null |
main
| 5.666667 |
4;5;8
| null | null |
Convolutional Sequence Modeling Revisited
| null | null | 0 | 3.666667 |
Workshop
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep metric learning;self-paced learning;representation learning;cnn
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
LEAP: Learning Embeddings for Adaptive Pace
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Ensemble Method;Adversarial Perturbations;Deep Neural Networks;Defense;Attack
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural 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 |
distributed deep learning;gradient compression;collective communication;data parallel distributed sgd;image classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Variance-based Gradient Compression for Efficient Distributed Deep Learning
| null | null | 0 | 4 |
Workshop
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
parametric;adversarial;divergence;generative;modeling;gan;neural;network;task;loss;structured;prediction
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Parametric Adversarial Divergences are Good Task Losses for Generative Modeling
| null | null | 0 | 3.333333 |
Workshop
|
3;4;3
| null |
null |
Institute of Natural Sciences, Shanghai Jiao Tong University; Department of Computer Science, University College London; London Centre for Nanotechnology, University College London; Department of Physics & Astronomy, University College London; GTN Ltd.
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Networks;Tensor Networks;Tensor Trains
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Compact Neural Networks based on the Multiscale Entanglement Renormalization Ansatz
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
DeepMind; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc G Bellemare, Remi Munos
|
https://iclr.cc/virtual/2018/poster/133
|
reinforcement learning;policy gradient;distributional reinforcement learning;distributed computing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
| null | null | 0 | 3.333333 |
Poster
|
4;2;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Justin Fu, Katie Luo, Sergey Levine
|
https://iclr.cc/virtual/2018/poster/148
|
inverse reinforcement learning;deep reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Learning Robust Rewards with Adverserial Inverse Reinforcement Learning
| null | null | 0 | 3 |
Poster
|
4;2;3
| null |
null |
Machine Perception, Google Research
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Shankar Krishnan, Ying Xiao, Rif A. Saurous
|
https://iclr.cc/virtual/2018/poster/174
|
Deep Learning;Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Boltzmann machine;bias-variance decomposition;information geometry
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Bias-Variance Decomposition for Boltzmann Machines
| null | null | 0 | 4 |
Reject
|
2;5;5
| null |
null |
DeepMind, London, UK
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher Burgess, Matko Bošnjak, Murray Shanahan, Matthew Botvinick, , Alexander Lerchner
|
https://iclr.cc/virtual/2018/poster/150
|
grounded visual concepts;compositional representation;concept hierarchy;disentangling;beta-VAE;variational autoencoder;deep learning;generative model
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
SCAN: Learning Hierarchical Compositional Visual Concepts
| 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 |
Reinforcement Learning;Robotics
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Overview on Reinforcement Learning for Robotics
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
Department of Computer Science and Engineering, Seoul National University, Seoul, Korea
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Youngjin Kim, Minjung Kim, Gunhee Kim
|
https://iclr.cc/virtual/2018/poster/292
|
Generative Adversarial Networks;Memory Networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks
| 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
| 6 |
5;6;7
| null | null |
Recursive Binary Neural Network Learning Model with 2-bit/weight Storage Requirement
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
Departments of Psychology and Computer Science, Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305; Department of Psychology, Stanford University, Stanford, CA 94305; Department of Physics, Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Kevin Feigelis, Blue Sheffer, Daniel L Yamins
|
https://iclr.cc/virtual/2018/poster/165
|
Continual Learning;Neural Modules;Interface Learning;Task Switching;Reinforcement Learning;Visual Decision Making
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
6;8;8
| null | null |
Modular Continual Learning in a Unified Visual Environment
| null | null | 0 | 2.333333 |
Poster
|
2;2;3
| null |
null |
OpenAI; Rutgers University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Tim Salimans, Han Zhang, Alec Radford, Dimitris Metaxas
|
https://iclr.cc/virtual/2018/poster/296
|
GAN;generative modeling;adversarial;optimal transport
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Improving GANs Using Optimal Transport
| null | null | 0 | 3 |
Poster
|
3;2;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Networks;Unsupervised Learning;GANs
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Generative Adversarial Networks using Adaptive Convolution
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
DNN representation;penalty regularization;channel coding
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
DNN Representations as Codewords: Manipulating Statistical Properties via Penalty Regularization
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
Department of Statistics, University of California, Berkeley; Department of Statistics, Department of EECS, University of California, Berkeley; Google Brain, Mountain View, CA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
William Murdoch, Peter J Liu, Bin Yu
|
https://iclr.cc/virtual/2018/poster/164
|
interpretability;LSTM;natural language processing;sentiment analysis;interactions
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
| null | null | 0 | 3 |
Oral
|
3;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
architecture search;deep learning;hyperparameter tuning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
DeepArchitect: Automatically Designing and Training Deep Architectures
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
null |
MILA, University of Montr´eal; MILA, University of Montr´eal, CIFAR, IV ADO; MSR; New York University, CIFAR Azrieli Global Scholar; MILA, University of Montr´eal, IV ADO; MILA, MSR, University of Waterloo
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
R Devon Hjelm, Athul Paul Jacob, Adam Trischler, Tong Che, Kyunghyun Cho, Yoshua Bengio
|
https://iclr.cc/virtual/2018/poster/151
|
Generative adversarial networks;generative learning;deep learning;neural networks;adversarial learning;discrete data
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
4;7;7
| null | null |
Boundary Seeking GANs
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised;gan;domain adaptation;style transfer;semantic;image translation;dataset
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
XGAN: Unsupervised Image-to-Image Translation for many-to-many Mappings
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
Facebook AI Research; Sorbonne Universit´es, UPMC Univ Paris 06, LIP6 UMR 7606, CNRS; Facebook AI Research, Sorbonne Universit´es, UPMC Univ Paris 06, LIP6 UMR 7606, CNRS
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Guillaume Lample, , Ludovic Denoyer, Marc'Aurelio Ranzato
|
https://iclr.cc/virtual/2018/poster/22
|
unsupervised;machine translation;adversarial
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Unsupervised Machine Translation Using Monolingual Corpora Only
| null | null | 0 | 4.666667 |
Poster
|
4;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
One-shot learning;embeddings;word embeddings;natural language processing;NLP
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
One-shot and few-shot learning of word embeddings
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
University of California San Diego; Pennsylvania State University; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Ulfar Erlingsson
|
https://iclr.cc/virtual/2018/poster/13
|
privacy;differential privacy;machine learning;deep learning
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Scalable Private Learning with PATE
| null | null | 0 | 2.666667 |
Poster
|
1;4;3
| null |
null |
Stanford University; Pennsylvania State University; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Florian Tramer, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel
|
https://iclr.cc/virtual/2018/poster/157
|
Adversarial Examples;Adversarial Training;Attacks;Defenses;ImageNet
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Ensemble Adversarial Training: Attacks and Defenses
| null | null | 0 | 3.333333 |
Poster
|
4;2;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised learning;supervised learning;knowledge representation;deep learning
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 5.333333 |
2;7;7
| null | null |
Feat2Vec: Dense Vector Representation for Data with Arbitrary Features
| null | null | 0 | 3.333333 |
Reject
|
2;5;3
| null |
null |
Facebook AI Research; UC Berkeley
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;generalization;navigation;3D scenes
| null | 0 | null | null |
iclr
| -0.693375 | 0 | null |
main
| 5.666667 |
4;5;8
| null | null |
Building Generalizable Agents with a Realistic and Rich 3D Environment
|
http://github.com/facebookresearch/House3D
| null | 0 | 4.333333 |
Workshop
|
5;4;4
| null |
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