pdf
stringlengths 49
199
⌀ | aff
stringlengths 1
1.36k
⌀ | year
stringclasses 19
values | technical_novelty_avg
float64 0
4
⌀ | video
stringlengths 21
47
⌀ | doi
stringlengths 31
63
⌀ | presentation_avg
float64 0
4
⌀ | proceeding
stringlengths 43
129
⌀ | presentation
stringclasses 796
values | sess
stringclasses 576
values | technical_novelty
stringclasses 700
values | arxiv
stringlengths 10
16
⌀ | author
stringlengths 1
1.96k
⌀ | site
stringlengths 37
191
⌀ | keywords
stringlengths 2
582
⌀ | oa
stringlengths 86
198
⌀ | empirical_novelty_avg
float64 0
4
⌀ | poster
stringlengths 57
95
⌀ | openreview
stringlengths 41
45
⌀ | conference
stringclasses 11
values | corr_rating_confidence
float64 -1
1
⌀ | corr_rating_correctness
float64 -1
1
⌀ | project
stringlengths 1
162
⌀ | track
stringclasses 3
values | rating_avg
float64 0
10
⌀ | rating
stringlengths 1
17
⌀ | correctness
stringclasses 809
values | slides
stringlengths 32
41
⌀ | title
stringlengths 2
192
⌀ | github
stringlengths 3
165
⌀ | authors
stringlengths 7
161
⌀ | correctness_avg
float64 0
5
⌀ | confidence_avg
float64 0
5
⌀ | status
stringclasses 22
values | confidence
stringlengths 1
17
⌀ | empirical_novelty
stringclasses 763
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Hierarchical reinforcement learning;temporal logic;skill composition
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
AUTOMATA GUIDED HIERARCHICAL REINFORCEMENT LEARNING FOR ZERO-SHOT SKILL COMPOSITION
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
anomaly detection;one class support vector machine;adversarial learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Unsupervised Adversarial Anomaly Detection using One-Class Support Vector Machines
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Knowledge Discovery;Generative Modeling;Medical;Entity Pair
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3.333333 |
2;4;4
| null | null |
Generative Discovery of Relational Medical Entity Pairs
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Samuel Smith, Quoc V Le
|
https://iclr.cc/virtual/2018/poster/289
|
generalization;stochastic gradient descent;stochastic differential equations;scaling rules;large batch training;bayes theorem;batch size
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
3;7;7
| null | null |
A Bayesian Perspective on Generalization and Stochastic Gradient Descent
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
Amsterdam Machine Learning Lab, University of Amsterdam, Amsterdam, 1098 XH, NL; Courant Institute of Mathematical Sciences, New York University, New York City, NY, 10010, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Victor Garcia Satorras, Joan Bruna
|
https://iclr.cc/virtual/2018/poster/43
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Few-Shot Learning with Graph Neural Networks
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
convolutional neural networks;image classification;deep learning;feature representation;hilbert maps;reproducing kernel hilbert space
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Continuous Convolutional Neural Networks for Image Classification
| null | null | 0 | 3 |
Reject
|
4;3;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
convolutional neural networks;loss surface;expressivity;critical point;global minima;linear separability
| null | 0 | null | null |
iclr
| -0.6742 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
The loss surface and expressivity of deep convolutional neural networks
| null | null | 0 | 2.75 |
Workshop
|
4;2;3;2
| null |
null |
RIKEN AIP, Tokyo, Japan; RIKEN AIP, Tokyo, Japan; The University of Tokyo, Tokyo, Japan; The University of Aveiro, Aveiro, Portugal
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Voot Tangkaratt, , Masashi Sugiyama
|
https://iclr.cc/virtual/2018/poster/186
|
Reinforcement learning;actor-critic;continuous control
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Guide Actor-Critic for Continuous Control
| null | null | 0 | 3.333333 |
Poster
|
4;2;4
| null |
null |
Massachusetts Institute of Technology
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Chulhee Yun, Suvrit Sra, Ali Jadbabaie
|
https://iclr.cc/virtual/2018/poster/90
|
deep linear neural networks;global optimality;deep learning
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Global Optimality Conditions for Deep Neural Networks
| null | null | 0 | 4.666667 |
Poster
|
5;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
uncertainty estimation;deep learning;Bayesian learning;batch normalization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Unseen Class Discovery in Open-world Classification
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
University of California, Davis, CA 95616, USA; Microsoft Research, Redmond, WA 98052, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Ke Wang, Rishabh Singh, Zhendong Su
|
https://iclr.cc/virtual/2018/poster/69
|
Program Embedding;Program Semantics;Dynamic Traces
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Dynamic Neural Program Embeddings for Program Repair
| null | null | 0 | 3 |
Poster
|
2;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
semi-supervised learning;image recognition;sequence tagging;dependency parsing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
2;5;7
| null | null |
Cross-View Training for Semi-Supervised Learning
| null | null | 0 | 4 |
Workshop
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised learning;clustering;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Neural Clustering By Predicting And Copying Noise
|
https://github.com/neuralclusteringNAT/paper-resources/tree/master/tweet-clustering
| 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;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
category representations;psychology;cognitive science;deep neural networks
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Capturing Human Category Representations by Sampling in Deep Feature Spaces
| null | null | 0 | 4.333333 |
Workshop
|
4;5;4
| null |
null |
Faculty of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA; Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Accelerating Neural Architecture Search using Performance Prediction
| null | null | 0 | 4 |
Workshop
|
3;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Model parallelism;Learning theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Continuous Propagation: Layer-Parallel Training
| null | null | 0 | 3.666667 |
Withdraw
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;TD Learning;DQN
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
2;3;4
| null | null |
TD Learning with Constrained Gradients
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
attention;sensor-selection;multi-sensor;natural noise
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
3;4;7
| null | null |
Sensor Transformation Attention 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 RL;Thompson Sampling;Posterior update
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Efficient Exploration through Bayesian Deep Q-Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
asymmetric structure;RNN-CNN;fast;unsupervised;representation;sentence
| null | 0 | null | null |
iclr
| -0.970725 | 0 | null |
main
| 5.333333 |
3;6;7
| null | null |
Exploring Asymmetric Encoder-Decoder Structure for Context-based Sentence Representation Learning
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
University of Massachusetts, Amherst, MA, USA; University of Alberta, Edmonton, AB, Canada; IBM Research, Yorktown Heights, NY, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, Murray Campbell
|
https://iclr.cc/virtual/2018/poster/201
|
reinforcement learning;options;successor representation;proto-value functions;Atari;Arcade Learning Environment
| null | 0 | null | null |
iclr
| 0.981981 | 0 | null |
main
| 7.333333 |
6;7;9
| null | null |
Eigenoption Discovery through the Deep Successor Representation
| null | null | 0 | 4 |
Poster
|
3;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Long-tail datasets;Imbalanced datasets
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Bayesian Embeddings for Long-Tailed Datasets
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null |
Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Emilio Parisotto, Ruslan Salakhutdinov
|
https://iclr.cc/virtual/2018/poster/196
|
deep reinforcement learning;deep learning;memory
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 7.333333 |
6;7;9
| null | null |
Neural Map: Structured Memory for Deep Reinforcement Learning
| null | null | 0 | 4.666667 |
Poster
|
5;4;5
| null |
null |
Boston University
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
domain adaptation;adversarial networks;statistical distance;duality
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Stable Distribution Alignment Using the Dual of the Adversarial Distance
| null | null | 0 | 3.666667 |
Workshop
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Convolutional Neural Networks;CNN;CP Decomposition;Low Rank Approximation
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Accelerating Convolutional Neural Networks using Iterative Two-Pass Decomposition
| null | null | 0 | 4.333333 |
Withdraw
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep reinforcement learning
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Regret Minimization for Partially Observable Deep Reinforcement Learning
| null | null | 0 | 4.333333 |
Workshop
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
predictive distribution estimation;probabilistic RNN;uncertainty in time series prediction
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Learning temporal evolution of probability distribution with Recurrent Neural Network
| null | null | 0 | 3.333333 |
Reject
|
4;2;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Non-convex optimization;Two-layer Neural Network;global optimality;first-order optimality
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
4;7;7
| null | null |
Theoretical properties of the global optimizer of two-layer Neural Network
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null |
Department of Electrical and Computer Engineering, University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Pouya Samangouei, Maya Kabkab, Rama Chellappa
|
https://iclr.cc/virtual/2018/poster/113
| null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
Borealis AI, Canada
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yanshuai Cao, Gavin Weiguang Ding, Yik Chau Lui, Ruitong Huang
|
https://iclr.cc/virtual/2018/poster/24
| null | null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Learning to learn;meta-learning;reinforcement learning;optimization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Learning to Optimize Neural Nets
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;natural language generation;discrete structure modeling;adversarial training;unaligned text style-transfer
| null | 0 | null | null |
iclr
| -0.80829 | 0 | null |
main
| 5.75 |
3;5;6;9
| null | null |
Adversarially Regularized Autoencoders
| null | null | 0 | 3.5 |
Workshop
|
4;4;3;3
| null |
null |
Facebook AI Research; Facebook AI Research, Tel Aviv University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yedid Hoshen, Lior Wolf
|
https://iclr.cc/virtual/2018/poster/226
|
unsupervised mapping;cross domain mapping
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Identifying Analogies Across Domains
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Verification;SMT solver;Mixed Integer Programming;Neural Networks
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Piecewise Linear Neural Networks verification: A comparative study
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
autoencoders;sequence models;discrete representations
| null | 0 | null | null |
iclr
| -0.720577 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Discrete Autoencoders for Sequence Models
| null | null | 0 | 3.333333 |
Reject
|
4;5;1
| null |
null |
Department of Engineering, University of Cambridge
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Viet Cuong Nguyen, Yingzhen Li, Thang Bui, Richard E Turner
|
https://iclr.cc/virtual/2018/poster/199
|
continual learning;online variational inference
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Variational Continual Learning
| null | null | 0 | 3 |
Poster
|
2;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Semi-supervised Learning;Generative And Adversary Framework;One-class classification;Outlier detection
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Semi-supervised Outlier Detection using Generative And Adversary Framework
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
Baidu Inc., Beijing, China; National Engineering Laboratory of Deep Learning Technology and Application, China; Baidu Inc., Beijing, China
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Chao Qiao, Bo Huang, Guocheng Niu, daren li, daxiang dong, wei he, Dianhai Yu, hua wu
|
https://iclr.cc/virtual/2018/poster/325
|
region embedding;local context unit;text classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
A New Method of Region Embedding for Text Classification
| null | null | 0 | 4 |
Poster
|
5;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Joint embeddings;Hard Negatives;Visual-semantic embeddings;Cross-modal retrieval;Ranking
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4
| null | null |
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
| null | null | 0 | 4 |
Withdraw
|
4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Program Synthesis;Semantic Parsing;WikiTable;SQL;Pointer Network
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
3;4;7
| null | null |
Pointing Out SQL Queries From Text
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
IBM Research AI, Yorktown Heights, NY 10598; Massachusetts Institute of Technology, Cambridge, MA 02139; Tencent AI Lab, Bellevue, WA 98004; University of California, Davis, CA 95616
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel
|
https://iclr.cc/virtual/2018/poster/97
|
robustness;adversarial machine learning;neural network;extreme value theory;adversarial example;adversarial perturbation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
| null | null | 0 | 2.333333 |
Poster
|
1;3;3
| null |
null |
Google AI; University of Illinois at Urbana-Champaign; Georgia Institute of Technology; Georgia Institute of Technology, Ant Financial Services Group
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Bo Dai, Albert Shaw, Niao He, Lihong Li, Le Song
|
https://iclr.cc/virtual/2018/poster/23
|
reinforcement learning;actor-critic algorithm;Lagrangian duality
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Boosting the Actor with Dual Critic
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
label embedding;deep learning;label representation;computer vision;natural language processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Continual Learning;Catastrophic Forgetting;Sequential Multitask Learning;Deep Generative Models;Dual Memory Networks;Deep Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Deep Generative Dual Memory Network for Continual Learning
| null | null | 0 | 3.333333 |
Reject
|
4;4;2
| null |
null |
The University of Texas at Austin and Sentient Technologies, Inc.
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Elliot Meyerson, Risto Miikkulainen
|
https://iclr.cc/virtual/2018/poster/49
|
multitask learning;deep learning;modularity
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Memory Networks;Combinatorial Optimization;Binary LP
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Long Term Memory Network for Combinatorial Optimization Problems
| null | null | 0 | 2.333333 |
Reject
|
4;2;1
| null |
null |
Facebook AI Research, New York, NY
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Alex Peysakhovich, Adam Lerer
|
https://iclr.cc/virtual/2018/poster/335
|
deep reinforcement learning;cooperation;social dilemma;multi-agent systems
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Consequentialist conditional cooperation in social dilemmas with imperfect information
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural networks;Training;Convergence
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
FastNorm: Improving Numerical Stability of Deep Network Training with Efficient Normalization
| null | null | 0 | 3.333333 |
Withdraw
|
3;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement learning;deep learning;autonomous control
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Combination of Supervised and Reinforcement Learning For Vision-Based Autonomous Control
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null |
Department of Computer Science, University of Toronto and FOR.ai; FOR.ai; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Aidan Gomez, Sicong(Sheldon) Huang, Ivan Zhang, Bryan Li, Muhammad Osama, Lukasz Kaiser
|
https://iclr.cc/virtual/2018/poster/277
| null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Unsupervised Cipher Cracking Using Discrete GANs
|
github.com/for-ai/ciphergan
| null | 0 | 3 |
Poster
|
1;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multi-agent Reinforcement Learning;Communication;Reward Distribution;Trusted Third Party;Auction Theory
| null | 0 | null | null |
iclr
| -0.960769 | 0 | null |
main
| 5.333333 |
3;6;7
| null | null |
Neuron as an Agent
| null | null | 0 | 4 |
Workshop
|
5;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Space-by-time non-negative matrix factorization;dimensionality reduction;baseline correction;neuronal decoding;mutual information
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Baseline-corrected space-by-time non-negative matrix factorization for decoding single trial population spike trains
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Approximate Inference;Amortization;Posterior Approximations;Variational Autoencoder
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Inference Suboptimality in Variational Autoencoders
| null | null | 0 | 4.666667 |
Workshop
|
4;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep reinforcement learning;navigation;path-planning;mapping
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
3;3;7
| null | null |
Do Deep Reinforcement Learning Algorithms really Learn to Navigate?
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
UC Berkeley
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Fred Shentu, Evan Shelhamer, Jitendra Malik, Alexei Efros, Trevor Darrell
|
https://iclr.cc/virtual/2018/poster/51
|
imitation;zero-shot;self-supervised;robotics;skills;navigation;manipulation;vizdoom;reinforcement
| null | 0 | null | null |
iclr
| -0.866025 | 0 |
https://pathak22.github.io/zeroshot-imitation/
|
main
| 7.666667 |
7;8;8
| null | null |
Zero-Shot Visual Imitation
|
https://github.com/pathak22/zeroshot-imitation
| null | 0 | 4 |
Oral
|
5;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adaptive learning rates;analytical continuation;fully connected networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
Per-Weight Class-Based Learning Rates via Analytical Continuation
| null | null | 0 | 3.333333 |
Withdraw
|
4;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
structured attention;neural machine translation;grammar induction
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
Inducing Grammars with and for Neural Machine Translation
| 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;invariance;data set;evaluation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
On the Construction and Evaluation of Color Invariant Networks
| 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
| 4.666667 |
4;5;5
| null | null |
Shifting Mean Activation Towards Zero with Bipolar Activation Functions
| null | null | 0 | 4 |
Workshop
|
4;5;3
| null |
null |
Paper under double-blind review
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Iterative temporal differencing;feedback alignment;spike-time dependent plasticity;vanilla backpropagation;deep learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 2.333333 |
2;2;3
| null | null |
Iterative temporal differencing with fixed random feedback alignment support spike-time dependent plasticity in vanilla backpropagation for deep learning
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
Computer Science Division, University of California, Berkeley
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Warren He, Bo Li, Dawn Song
|
https://iclr.cc/virtual/2018/poster/57
|
adversarial machine learning;supervised representation learning;decision regions;decision boundaries
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Decision Boundary Analysis of Adversarial Examples
| null | null | 0 | 2.666667 |
Poster
|
3;3;2
| null |
null |
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
LU HOU, James Kwok
|
https://iclr.cc/virtual/2018/poster/203
|
deep learning;network quantization
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Loss-aware Weight Quantization of Deep Networks
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
University of British Columbia; University of Oxford
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Atilim Gunes Baydin, Robert Cornish, David Martínez, Mark Schmidt, Frank Wood
|
https://iclr.cc/virtual/2018/poster/14
| null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Online Learning Rate Adaptation with Hypergradient Descent
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;wasserstein distance;discrete probability distribution
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Discrete Wasserstein Generative Adversarial Networks (DWGAN)
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
Facebook AI Research; Computer Science Department, Duke University; Data Sciences and Operations Department, University of Southern California
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Rong Ge, Jason Lee, Tengyu Ma
|
https://iclr.cc/virtual/2018/poster/119
|
theory;non-convex optimization;loss surface
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
6;7;9
| null | null |
Learning One-hidden-layer Neural Networks with Landscape Design
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null |
School of Informatics, University of Edinburgh, UK and Alan Turing Institute, London, UK; School of Informatics, University of Edinburgh, UK
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Cian Eastwood, Chris Williams
|
https://iclr.cc/virtual/2018/poster/55
| null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
A Framework for the Quantitative Evaluation of Disentangled Representations
| null | null | 0 | 4.666667 |
Poster
|
4;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
gaussian process neuron activation function stochastic transfer function learning variational bayes probabilistic
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Gaussian Process Neurons
| null | null | 0 | 3.666667 |
Reject
|
5;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multimodal;knowledge base;relational modeling;embedding;link prediction;neural network encoders
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Embedding Multimodal Relational Data
| null | null | 0 | 4.333333 |
Withdraw
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
DNN Compression;Weigh-sharing;Model Compression
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
DNN Model Compression Under Accuracy Constraints
| null | null | 0 | 3.666667 |
Reject
|
3;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
query completion;realtime;error correction;recurrent network;beam search
| null | 0 | null | null |
iclr
| -0.866025 | 0 |
http://www.deepquerycompletion.com
|
main
| 5 |
4;5;6
| null | null |
Realtime query completion via deep language models
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neuro-symbolic reasoning;information extraction;learn to search
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
LEARNING TO ORGANIZE KNOWLEDGE WITH N-GRAM MACHINES
| null | null | 0 | 3.666667 |
Workshop
|
4;3;4
| null |
null |
Sorbonne Universités, UMR 7606, LIP6, F-75005 Paris, France
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Emmanuel d Bezenac, Arthur Pajot, patrick Gallinari
|
https://iclr.cc/virtual/2018/poster/40
|
deep learning;physical processes;forecasting;spatio-temporal
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
| null | null | 0 | 2.666667 |
Poster
|
2;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Affect lexicon;word embeddings;Word2Vec;GloVe;WordNet;joint learning;sentiment analysis;word similarity;outlier detection;affect prediction
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Towards Building Affect sensitive Word Distributions
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;quantized deep neural network;activation quantization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
PACT: Parameterized Clipping Activation for Quantized Neural 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 |
Attention Model;Machine Comprehension;Question Answering
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;5;8
| null | null |
Phase Conductor on Multi-layered Attentions for Machine Comprehension
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
Karlsruhe Institute of Technology (KIT); University of California, Berkeley; OpenAI
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, Marcin Andrychowicz
|
https://iclr.cc/virtual/2018/poster/228
|
reinforcement learning;exploration;parameter noise
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Parameter Space Noise for Exploration
| 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 learning;homomorphic encryption;hybrid homomorphic encryption;privacy preserving;representation learning;neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Deep Learning Inferences with Hybrid Homomorphic Encryption
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null |
Department of Computer Engineering, Ulsan National Institute of Science and Technology, 50 UNIST, Ulsan 44919, Republic of Korea
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Information Bottleneck;Deep Neural Networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Parametric Information Bottleneck to Optimize Stochastic 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;Derivative Calculations;Optimization Algorithms
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
2;4;5
| null | null |
Understanding and Exploiting the Low-Rank Structure of Deep Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;external memory;deep learning;policy gradient;online learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Integrating Episodic Memory into a Reinforcement Learning Agent Using Reservoir Sampling
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
University of Oxford; DeepMind
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Gábor Melis, Chris Dyer, Phil Blunsom
|
https://iclr.cc/virtual/2018/poster/214
|
rnn;language modelling
| null | 0 | null | null |
iclr
| -0.785714 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
On the State of the Art of Evaluation in Neural Language Models
| null | null | 0 | 3.333333 |
Poster
|
5;2;3
| null |
null |
Department of Civil and Environmental Engineering, University of Southern California; Department of Computer Science, University of Southern California
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Sungyong Seo, Arash Mohegh, George Ban-Weiss, Yan Liu
|
https://iclr.cc/virtual/2018/poster/327
|
spatiotemporal data;graph convolutional network;data quality
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Automatically Inferring Data Quality for Spatiotemporal Forecasting
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;data augmentation;regularization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Data augmentation instead of explicit regularization
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multi-instance learning;Medical Time Series;Concept Annotation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;3;6
| null | null |
Relational Multi-Instance Learning for Concept Annotation from Medical Time Series
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null |
Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Engineering Science, University of Oxford, Oxford, UK; Alan Turing Institute, London, UK; Department of Engineering Science, University of Oxford, Oxford, UK
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Jinsung Yoon, James Jordan, Mihaela v Schaar
|
https://iclr.cc/virtual/2018/poster/153
|
Individualized Treatment Effects;Counterfactual Estimation;Generative Adversarial Nets
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
nearest neighbor;reinforcement learning;policy;continuous control
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Simple Nearest Neighbor Policy Method for Continuous Control Tasks
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
William Fedus, Ian Goodfellow, Andrew Dai
|
https://iclr.cc/virtual/2018/poster/10
|
Deep learning;GAN
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
MaskGAN: Better Text Generation via Filling in the _______
| null | null | 0 | 4 |
Poster
|
3;5;4
| null |
null |
Department of Computer Science, University of Southern California
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Michael Tsang, Dehua Cheng, Yan Liu
|
https://iclr.cc/virtual/2018/poster/285
|
statistical interaction detection;multilayer perceptron;generalized additive model
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Detecting Statistical Interactions from Neural Network Weights
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
Computer Science, UIUC, Urbana, IL 61801
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Tanmay Gangwani, Jian Peng
|
https://iclr.cc/virtual/2018/poster/160
|
Genetic algorithms;deep reinforcement learning;imitation learning
| null | 0 | null | null |
iclr
| 0.802955 | 0 | null |
main
| 5.666667 |
3;6;8
| null | null |
Policy Optimization by Genetic Distillation
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural architecture search
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Faster Discovery of Neural Architectures by Searching for Paths in a Large Model
| null | null | 0 | 2.333333 |
Workshop
|
3;2;2
| null |
null |
DeepMind; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew Dai, Shakir Mohamed, Ian Goodfellow
|
https://iclr.cc/virtual/2018/poster/180
|
Deep learning;GAN
| null | 0 | null | null |
iclr
| 0.240192 | 0 | null |
main
| 6.333333 |
4;7;8
| null | null |
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null |
Georgia Tech Research Institute, Atlanta, GA 30318, USA; Georgia Institute of Technology, Atlanta, GA 30332, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira
|
https://iclr.cc/virtual/2018/poster/333
|
transfer learning;similarity prediction;clustering;domain adaptation;unsupervised learning;computer vision;deep learning;constrained clustering
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
5;7;9
| null | null |
Learning to cluster in order to transfer across domains and tasks
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
Preferred Networks, Inc.; Ritsumeikan University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Takeru Miyato, Masanori Koyama
|
https://iclr.cc/virtual/2018/poster/217
|
Generative Adversarial Networks;GANs;conditional GANs;Generative models;Projection
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
cGANs with Projection Discriminator
|
https://github.com/pfnet-research/sngan_projection
| null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
University of Toronto, Vector Institute; General Motors Advanced Technical Center - Israel, Department of Electrical Engineering, Technion; General Motors Advanced Technical Center - Israel
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Oran Shayer, Dan Levi, Ethan Fetaya
|
https://iclr.cc/virtual/2018/poster/314
|
deep learning;discrete weight network
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Learning Discrete Weights Using the Local Reparameterization Trick
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
LSTM;RNN;rotation matrix;long-term memory;natural language processing
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Modifying memories in a Recurrent Neural Network Unit
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multiscale models;hidden Markov model;covariance prediction
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Multiscale Hidden Markov Models For Covariance Prediction
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
distribution regression;supervised learning;regression analysis
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Distribution Regression Network
| null | null | 0 | 3.333333 |
Reject
|
4;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Language AI Learning Reinforcement Deep
| null | 0 | null | null |
iclr
| -0.327327 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Understanding Grounded Language Learning Agents
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.