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 |
Under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
long-tailed learning;re-sampling;sample memorization
| null | 0 | null | null |
iclr
| -0.703526 | 0 | null |
main
| 4.5 |
2;5;5;6
| null | null |
The Unreasonable Effectiveness of the Class-reversed Sampling in Tail Sample Memorization
| null | null | 0 | 4.25 |
Reject
|
5;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Compression;sketching
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
5;6;7
| null | null |
A framework for learned CountSketch
| null | null | 0 | 3 |
Reject
|
3;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Safe RL;Probabilistic Distance Metrics
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.75 |
3;4;6;6
| null | null |
Safety Aware Reinforcement Learning (SARL)
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement learning;deep learning;financial engineering;optimal stopping.
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.75 |
2;4;4;5
| null | null |
Deep Reinforcement Learning for Optimal Stopping with Application in Financial Engineering
| null | null | 0 | 4 |
Withdraw
|
4;4;4;4
| null |
null |
Department of Physics and Institute for Biomedical Engineering, University of Toronto; Department of Physics, University of Toronto
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3045; None
| null | 0 | null | null | null | null | null |
Matthew Smart, Anton Zilman
|
https://iclr.cc/virtual/2021/poster/3045
|
Hopfield Networks;Restricted Boltzmann Machines;Statistical Physics
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
7;7;10
| null |
https://iclr.cc/virtual/2021/poster/3045
|
On the mapping between Hopfield networks and Restricted Boltzmann Machines
| null | null | 0 | 4 |
Oral
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
binary neural network;gradient masking;fake robustness;temperature scaling;adversarial attack;signal propagation
| null | 0 | null | null |
iclr
| 0.428571 | 0 | null |
main
| 5.8 |
5;5;6;6;7
| null | null |
Improved Gradient based Adversarial Attacks for Quantized Networks
| null | null | 0 | 4.2 |
Reject
|
4;3;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
identifiability analysis;deep learning;representation learning;probabilistic discriminative models
| null | 0 | null | null |
iclr
| -0.927173 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
On Linear Identifiability of Learned Representations
| null | null | 0 | 3.25 |
Reject
|
4;3;3;3
| null |
null |
OpenAI, San Francisco, CA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2790; None
| null | 0 | null | null | null | null | null |
Rewon Child
|
https://iclr.cc/virtual/2021/poster/2790
|
VAE;generative modeling;deep learning;likelihood-based models
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 7.5 |
7;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2790
|
Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
|
https://github.com/openai/vdvae
| null | 0 | 4.25 |
Spotlight
|
4;4;5;4
| null |
null |
Dept. ESAT, Center for Processing Speech and Images, KU Leuven, Belgium
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3194; None
| null | 0 | null | null | null | null | null |
Junyi Zhu, Matthew Blaschko
|
https://iclr.cc/virtual/2021/poster/3194
|
privacy leakage from gradients;federated learning;collaborative learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.666667 |
6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3194
|
R-GAP: Recursive Gradient Attack on Privacy
|
https://github.com/JunyiZhu-AI/R-GAP
| null | 0 | 3 |
Poster
|
2;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Multi-View Disentangled Representation
| null | null | 0 | 3.75 |
Reject
|
5;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Line graph
| null | 0 | null | null |
iclr
| 0.801784 | 0 | null |
main
| 5.2 |
4;5;5;6;6
| null | null |
Weighted Line Graph Convolutional Networks
| null | null | 0 | 3.8 |
Reject
|
3;4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Phase Retrieval;inverse problems;Symmetry;Deep Learning
| null | 0 | null | null |
iclr
| -0.852803 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Inverse Problems, Deep Learning, and Symmetry Breaking
| null | null | 0 | 3.75 |
Withdraw
|
5;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Parameter Perturbation;Reparameterization;Invertible Neural Networks;Normalizing Flows;Rank-one update
| null | 0 | null | null |
iclr
| -0.862662 | 0 | null |
main
| 4.75 |
2;5;6;6
| null | null |
Training Invertible Linear Layers through Rank-One Perturbations
| null | null | 0 | 3 |
Reject
|
5;3;1;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.40452 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Outlier Preserving Distribution Mapping Autoencoders
| null | null | 0 | 3.75 |
Reject
|
5;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples;unrestricted attacks;generative models;adversarial training;generative adversarial networks
| null | 0 | null | null |
iclr
| -0.789474 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Fine-grained Synthesis of Unrestricted Adversarial Examples
| null | null | 0 | 3.25 |
Reject
|
5;2;3;3
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3237; None
| null | 0 | null | null | null | null | null |
Jianhao Wang, Zhizhou Ren, Terry Liu, Yang Yu, Chongjie Zhang
|
https://iclr.cc/virtual/2021/poster/3237
|
Multi-agent reinforcement learning;Value factorization;Dueling structure
| null | 0 | null | null |
iclr
| -0.473684 | 0 | null |
main
| 5.75 |
4;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3237
|
QPLEX: Duplex Dueling Multi-Agent Q-Learning
| null | null | 0 | 3.75 |
Poster
|
4;4;5;2
| null |
null |
Department of Computer Science, University of Bristol, Bristol, UK, F94W 9Q
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2890; None
| null | 0 | null | null | null | null | null |
Laurence Aitchison
|
https://iclr.cc/virtual/2021/poster/2890
|
Bayesian inference;cold posteriors;sgld
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;6;7;9
| null |
https://iclr.cc/virtual/2021/poster/2890
|
A statistical theory of cold posteriors in deep neural networks
| null | null | 0 | 3.75 |
Poster
|
4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference
| null | null | 0 | 2.333333 |
Reject
|
1;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Online learning;Online Convex Optimization;Mirror Descent
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
On the Dynamic Regret of Online Multiple Mirror Descent
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
robustness;out-of-distribution generalization;natural variation;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
| null | null |
Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data
| null | null | 0 | 3 |
Reject
|
3;4;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Uncertainty modelling;Deep Learning;Black Box;Aleatoric;Quantile Regression;Chebyshev Polynomial;Neural networks
| null | 0 | null | null |
iclr
| 0.547399 | 0 | null |
main
| 5.2 |
2;4;6;7;7
| null | null |
ChePAN: Constrained Black-Box Uncertainty Modelling with Quantile Regression
| null | null | 0 | 2.8 |
Reject
|
2;2;4;4;2
| null |
null |
NEC Corporation, RIKEN Center for Advanced Intelligence Project (AIP); NEC Corporation
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3247; None
| null | 0 | null | null | null | null | null |
Akinori Ebihara, Taiki Miyagawa, Kazuyuki Sakurai, Hitoshi Imaoka
|
https://iclr.cc/virtual/2021/poster/3247
|
Sequential probability ratio test;Early classification;Density ratio estimation
| null | 0 | null | null |
iclr
| 0.320256 | 0 | null |
main
| 7.4 |
6;7;7;8;9
| null |
https://iclr.cc/virtual/2021/poster/3247
|
Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy
|
https://github.com/TaikiMiyagawa/SPRT-TANDEM
| null | 0 | 3.6 |
Spotlight
|
4;3;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
test-time adaptation;adversarial robustness;unsupervised domain adaptation;threat model;maximin game
| null | 0 | null | null |
iclr
| -0.96833 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
Test-Time Adaptation and Adversarial Robustness
| null | null | 0 | 3.25 |
Reject
|
4;4;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
LSTM;RNN;mass-conservation;neural arithmetic units;inductive bias;hydrology
| null | 0 | null | null |
iclr
| 0 | 0 |
Not provided
|
main
| 6.75 |
6;7;7;7
| null | null |
MC-LSTM: Mass-conserving LSTM
|
Not provided
| null | 0 | 3 |
Reject
|
3;3;3;3
| null |
null |
Department of Computer Science, University of Texas at Austin; Department of Statistics, The University of Chicago
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2904; None
| null | 0 | null | null | null | null | null |
Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae
|
https://iclr.cc/virtual/2021/poster/2904
|
causal inference;continuous treatment effect;doubly robustness
| null | 0 | null | null |
iclr
| -0.27735 | 0 | null |
main
| 6.666667 |
5;6;9
| null |
https://iclr.cc/virtual/2021/poster/2904
|
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments
| null | null | 0 | 4.333333 |
Oral
|
4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Imitation Learning;Reinforcement Learning;Density Estimation;Density Model;Maximum Entropy RL;Mujoco
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 6 |
5;5;6;8
| null | null |
Imitation with Neural Density Models
| null | null | 0 | 3.5 |
Reject
|
4;4;3;3
| null |
null |
University of California, San Diego; Rutgers University; ETRI
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3373; None
| null | 0 | null | null | null | null | null |
Fei Deng, Zhuo Zhi, Donghun Lee, Sungjin Ahn
|
https://iclr.cc/virtual/2021/poster/3373
|
object-centric representations;generative modeling;scene generation;variational autoencoders
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 5.5 |
4;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/3373
|
Generative Scene Graph Networks
| null | null | 0 | 3.5 |
Poster
|
3;4;3;4
| null |
null |
Courant Institute of Mathematical Sciences, New York University, New York, NY 10011, USA; Courant Institute of Mathematical Sciences, New York University, New York, NY 10011, USA; Center for Data Science, New York University, New York, NY 10011, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2765; None
| null | 0 | null | null | null | null | null |
Richard Yuanzhe Pang, He He
|
https://iclr.cc/virtual/2021/poster/2765
|
text generation;learning from demonstrations;nlp
| null | 0 | null | null |
iclr
| -0.25 | 0 | null |
main
| 6.6 |
5;7;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2765
|
Text Generation by Learning from Demonstrations
| null | null | 0 | 3.8 |
Poster
|
4;4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;novel classes;clustering;self-supervised learning;unsupervised learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;5;6
| null | null |
A Flexible Framework for Discovering Novel Categories with Contrastive Learning
| null | null | 0 | 3.6 |
Reject
|
4;3;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computation graph;Resource;Computer vision;Deep learning;Framework;Software
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.2 |
3;3;3;3;4
| null | null |
VideoFlow: A Framework for Building Visual Analysis Pipelines
|
https://github.com/xxx/videoflow
| null | 0 | 3.8 |
Reject
|
4;4;4;4;3
| null |
null |
Aurora Innovation Inc.; University of Washington
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2872; None
| null | 0 | null | null | null | null | null |
Mohak Bhardwaj, Sanjiban Choudhury, Byron Boots
|
https://iclr.cc/virtual/2021/poster/2872
|
reinforcement learning;model-predictive control
| null | 0 | null | null |
iclr
| 0.707107 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2872
|
Blending MPC & Value Function Approximation for Efficient Reinforcement Learning
| null | null | 0 | 3.5 |
Poster
|
3;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
manifold learning;autoencoders
| null | 0 | null | null |
iclr
| 0.345857 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Isometric Autoencoders
| null | null | 0 | 3.25 |
Reject
|
3;4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.666667 | 0 | null |
main
| 3.8 |
3;3;3;5;5
| null | null |
TOWARDS NATURAL ROBUSTNESS AGAINST ADVERSARIAL EXAMPLES
| null | null | 0 | 4.4 |
Reject
|
5;5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
efficient deep learning;meta learning;efficient training;data compression;instance selection
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null | null |
Stochastic Subset Selection for Efficient Training and Inference of Neural Networks
| null | null | 0 | 3.75 |
Reject
|
4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
dynamic network;data-dependent;complete graph
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.25 |
3;6;6;6
| null | null |
Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Meta reinforcement learning;Exploration;Information gain
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4;4
| null | null |
Intrinsically Guided Exploration in Meta Reinforcement Learning
| null | null | 0 | 3.75 |
Reject
|
5;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multi-agent reinforcement learning;policy optimization;advantage estimation;credit assignment
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation
| null | null | 0 | 3.5 |
Reject
|
4;4;4;2
| null |
null | null |
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2839; None
| null | 0 | null | null | null | null | null |
86152
|
https://iclr.cc/virtual/2021/poster/2839
| null | null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 7.25 |
6;7;8;8
| null |
https://iclr.cc/virtual/2021/poster/2839
|
Locally Free Weight Sharing for Network Width Search
| null | null | 0 | 4.25 |
Spotlight
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Imitation Learning;Reinforcement Learning;Universal Value Functions
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement Learning
| null | null | 0 | 3.75 |
Reject
|
4;4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
offline;meta-reinforcement learning;meta-learning;reinforcement learning;maml
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Offline Meta-Reinforcement Learning with Advantage Weighting
| null | null | 0 | 3.75 |
Reject
|
4;5;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Action recognition;Video understanding;Motion analysis
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Learning Self-Similarity in Space and Time as a Generalized Motion for Action Recognition
| null | null | 0 | 3.75 |
Reject
|
3;4;4;4
| null |
null |
RIKEN CBS; Columbia University; University of Toronto, Vector Institute; University of Tokyo, RIKEN AIP; Google Research, Brain Team
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3104; None
| null | 0 | null | null | null | null | null |
Shun-ichi Amari, Jimmy Ba, Roger Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu
|
https://iclr.cc/virtual/2021/poster/3104
|
generalization;second-order optimization;natural gradient descent;high-dimensional asymptotics
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3104
|
When does preconditioning help or hurt generalization?
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Quantile Regression;Generative Adversarial Networks (GANs);Frechet Inception Distance (FID);Generative Neural Networks
| null | 0 | null | null |
iclr
| -0.514496 | 0 | null |
main
| 3.2 |
2;2;3;4;5
| null | null |
QRGAN: Quantile Regression Generative Adversarial Networks
| null | null | 0 | 4.2 |
Reject
|
5;4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Novel Class Generalization;Finetuning One Scale Vector;Adaptive Feature Distribution;Cross-Domain
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 3.25 |
2;3;4;4
| null | null |
Improve Novel Class Generalization By Adaptive Feature Distribution for Few-Shot Learning
| null | null | 0 | 4.25 |
Withdraw
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Backdoor Attack;Backdoor Defense;Security;Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Rethinking the Trigger of Backdoor Attack
| null | null | 0 | 3.666667 |
Withdraw
|
3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Multi–objective Optimization;Adversarial Machine Learning;Bayesian Optimization
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.5 |
3;5;5;5
| null | null |
Approximating Pareto Frontier through Bayesian-optimization-directed Robust Multi-objective Reinforcement Learning
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multitask learning;shared representations;generalization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Multiaffine representations mediate tradeoff between generalization and parallel processing capacity in networks trained to multitask
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
Yonsei University; Seoul National University; Sungkyunkwan University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3355; None
| null | 0 | null | null | null | null | null |
Sungmin Cha, Taeeon Park, Byeongjoon Kim, Jongduk Baek, Taesup Moon
|
https://iclr.cc/virtual/2021/poster/3355
|
blind denoising;unsupervised learning;iterative training;generative learning
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 6.25 |
4;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/3355
|
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images
|
https://github.com/csm9493/GAN2GAN
| null | 0 | 3.75 |
Poster
|
3;4;3;5
| null |
null |
Mila, University of Montreal; Max Planck Institute for Intelligent Systems; ETH Zurich
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2579; None
| null | 0 | null | null | null | null | null |
Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wuthrich, Yoshua Bengio, Bernhard Schoelkopf, Stefan Bauer
|
https://iclr.cc/virtual/2021/poster/2579
|
reinforcement learning;transfer learning;sim2real transfer;domain adaptation;causality;generalization;robotics
| null | 0 | null | null |
iclr
| -0.169031 | 0 |
https://sites.google.com/view/causal-world/home
|
main
| 6.25 |
4;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2579
|
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning
| null | null | 0 | 3.5 |
Poster
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
ConvNet robustness;data corruption;inhibition;push-pull
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Inhibition-augmented ConvNets
| null | null | 0 | 4 |
Withdraw
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
BERT Regularization;Reinforcement Learning;Automated Regularization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
AUBER: Automated BERT Regularization
| null | null | 0 | 4.5 |
Reject
|
5;4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Pre-training;Knowledge Graph;Language Understanding;Graph Neural Network
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
JAKET: Joint Pre-training of Knowledge Graph and Language Understanding
| null | null | 0 | 4.5 |
Reject
|
4;5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Binary Neural Networks;Sparsity;Deep Neural Network Compression
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Sparse Binary Neural Networks
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
program representations;program analysis;compilers;graph neural networks
| null | 0 | null | null |
iclr
| -0.840168 | 0 | null |
main
| 6.2 |
4;6;7;7;7
| null | null |
Deep Data Flow Analysis
| null | null | 0 | 3.4 |
Reject
|
4;4;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adaptive Neural Network;Slimmable Neural Network;Channel Optimization;Neural Architecture Search;Convolutional Neural Network;Image Classification
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
PareCO: Pareto-aware Channel Optimization for Slimmable Neural Networks
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Nondeterminism;Instability
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
On Nondeterminism and Instability in Neural Network Optimization
| null | null | 0 | 3.5 |
Reject
|
3;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Adversarial Networks;Lightweight;Long-Range Dependency
| null | 0 | null | null |
iclr
| -0.681385 | 0 | null |
main
| 4.6 |
3;4;5;5;6
| null | null |
Lightweight Long-Range Generative Adversarial Networks
| null | null | 0 | 4.2 |
Withdraw
|
5;4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
normalizing flow;probabilistic inference;variational inference;inverse problem
| null | 0 | null | null |
iclr
| -0.316228 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Approximate Probabilistic Inference with Composed Flows
| null | null | 0 | 4 |
Reject
|
4;5;3;4
| null |
null |
The University of Edinburgh; Beijing Institute of Big Data Research; MILA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3267; None
| null | 0 | null | null | null | null | null |
Yanzhi Chen, Dinghuai Zhang, Michael U Gutmann, Aaron Courville, Zhanxing Zhu
|
https://iclr.cc/virtual/2021/poster/3267
|
likelihood-free inference;bayesian inference;mutual information;representation learning;summary statistics
| null | 0 | null | null |
iclr
| -0.707107 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3267
|
Neural Approximate Sufficient Statistics for Implicit Models
|
https://github.com/cyz-ai/neural-approx-ss-lfi
| null | 0 | 4 |
Spotlight
|
5;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
loss function;gradient descent;roubst deep learning;example weighting;regularization;label noise;sample imbalance
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Derivative Manipulation for General Example Weighting
| null | null | 0 | 4 |
Withdraw
|
4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Regression;Noisy labels;Supervised Learning;Uncertainty;Variance;Heteroscedastic;Privileged Information
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Batch Inverse-Variance Weighting: Deep Heteroscedastic Regression
| null | null | 0 | 4 |
Reject
|
3;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural modular networks;video-grounded dialogues;dialogue understanding;video understanding;video QA;video-grounded language tasks
| null | 0 | null | null |
iclr
| -0.258199 | 0 | null |
main
| 4.75 |
4;5;5;5
| null | null |
VilNMN: A Neural Module Network approach to Video-Grounded Language Tasks
| null | null | 0 | 3.5 |
Reject
|
4;5;3;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Curriculum learning;neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.5 |
3;3;4;4
| null | null |
A Simple Approach To Define Curricula For Training Neural Networks
| null | null | 0 | 4 |
Reject
|
4;4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings
| null | null | 0 | 4 |
Reject
|
3;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Fourier transform;time series;signal processing;anomaly detection;machine learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Fast Partial Fourier Transform
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null |
Unknown
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2895; None
| null | 0 | null | null | null | null | null |
James Lucas, Mengye Ren, Irene Raissa KAMENI KAMENI, Toniann Pitassi, Richard Zemel
|
https://iclr.cc/virtual/2021/poster/2895
|
meta learning;few-shot;minimax risk;lower bounds;learning theory
| null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2895
|
Theoretical bounds on estimation error for meta-learning
| null | null | 0 | 3.75 |
Poster
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
large scale distributed deep learning;second order optimization;bert;resnet;criteo;transformer;machine translation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Towards Practical Second Order Optimization for Deep Learning
| null | null | 0 | 3 |
Reject
|
3;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep neural networks;Theory of deep networks;deep regularization;Neural network compression
| null | 0 | null | null |
iclr
| -0.763763 | 0 | null |
main
| 4.8 |
4;4;5;5;6
| null | null |
Better Together: Resnet-50 accuracy with $13 \times $ fewer parameters and at $3 \times $ speed
| null | null | 0 | 3.6 |
Reject
|
4;4;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Prior Knowledge;Universal Representation;Self-Attention Networks;Neural Machine Translation
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Prior Knowledge Representation for Self-Attention Networks
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
uncertainty estimation;one -to-many mapping;conditional generative model;discrete latent space;medical image segmentation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Modal Uncertainty Estimation via Discrete Latent Representations
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 3.75 |
3;4;4;4
| null | null |
FASG: Feature Aggregation Self-training GCN for Semi-supervised Node Classification
| null | null | 0 | 4.5 |
Reject
|
5;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Data Imbalance;Classification;Semantic Segmentation;Deep Learning
| null | 0 | null | null |
iclr
| 0.852803 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Recall Loss for Imbalanced Image Classification and Semantic Segmentation
| null | null | 0 | 4.25 |
Reject
|
3;5;4;5
| null |
null |
University of Washington; ByteDance Inc.; KAUST
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3054; None
| null | 0 | null | null | null | null | null |
Yuchen Jin, Tianyi Zhou, Liangyu Zhao, Yibo Zhu, Chuanxiong Guo, Marco Canini, Arvind Krishnamurthy
|
https://iclr.cc/virtual/2021/poster/3054
| null | null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3054
|
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly
| null | null | 0 | 3.5 |
Poster
|
4;2;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Dependency Parsing;Unsupervised Constituency Parsing;Masked Language Model
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
factorized linear discriminant analysis;phenotype;gene expression;representation learning
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Factorized linear discriminant analysis for phenotype-guided representation learning of neuronal gene expression data
| null | null | 0 | 3 |
Reject
|
3;2;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hierarchical classification;prototypical networks
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 4.5 |
4;4;4;6
| null | null |
Leveraging Class Hierarchies with Metric-Guided Prototype Learning
| null | null | 0 | 3.75 |
Reject
|
4;5;3;3
| null |
null |
Harvard University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2603; None
| null | 0 | null | null | null | null | null |
Yamini Bansal, Gal Kaplun, Boaz Barak
|
https://iclr.cc/virtual/2021/poster/2603
|
Deep Learning Theory;Generalization Bounds;Self-supervised learning;Representation learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.25 |
3;4;7;7
| null |
https://iclr.cc/virtual/2021/poster/2603
|
For self-supervised learning, Rationality implies generalization, provably
| null | null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null |
Stanford University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2804; None
| null | 0 | null | null | null | null | null |
Jiaming Song, Chenlin Meng, Stefano Ermon
|
https://iclr.cc/virtual/2021/poster/2804
|
generative models;variational autoencoders;denoising score matching;variational inference
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null |
https://iclr.cc/virtual/2021/poster/2804
|
Denoising Diffusion Implicit Models
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
causality;representation learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Efficiently Disentangle Causal Representations
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Continual Learning;Federated Learning;Deep Learning
| null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Federated Continual Learning with Weighted Inter-client Transfer
| null | null | 0 | 4.25 |
Reject
|
4;5;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
long tailed recognition;network calibration;label-aware smoothing;mixup;dataset bias
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Improving Calibration for Long-Tailed Recognition
| null | null | 0 | 3.666667 |
Withdraw
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.96225 | 0 | null |
main
| 5.25 |
4;4;6;7
| null | null |
Information Lattice Learning
| null | null | 0 | 3.5 |
Reject
|
4;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Federated Learning;Communication-Bounded Learning
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Model-Agnostic Round-Optimal Federated Learning via Knowledge Transfer
| null | null | 0 | 4.25 |
Reject
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
BatchMean Triplet Loss;Semi-Supervised Learning;Consistency Regularization;Metric Learning
| null | 0 | null | null |
iclr
| -0.6742 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
RankingMatch: Delving into Semi-Supervised Learning with Consistency Regularization and Ranking Loss
| null | null | 0 | 4.25 |
Reject
|
5;5;3;4
| null |
null |
Qualcomm AI Research, New York University; Qualcomm AI Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2556; None
| null | 0 | null | null | null | null | null |
Marc Finzi, Roberto Bondesan, Max Welling
|
https://iclr.cc/virtual/2021/poster/2556
|
probabilistic numerics;gaussian processes;discretization error;pde;superpixel;irregularly spaced time series;misssing data;spatial uncertainty
| null | 0 | null | null |
iclr
| -0.662266 | 0 | null |
main
| 6.75 |
6;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2556
|
Probabilistic Numeric Convolutional Neural Networks
| null | null | 0 | 2.75 |
Poster
|
4;3;1;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Learning from web data;video action recognition;network pre-training
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Exploring Sub-Pseudo Labels for Learning from Weakly-Labeled Web Videos
| null | null | 0 | 4.333333 |
Withdraw
|
4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
initialization;optimization
| null | 0 | null | null |
iclr
| 0.94388 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks
| null | null | 0 | 3.25 |
Reject
|
2;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
zeroth-order optimization;online learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;4;4;8
| null | null |
Boosting One-Point Derivative-Free Online Optimization via Residual Feedback
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
visual representation learning;contrastive learning;medical image understanding;natural language processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Contrastive Learning of Medical Visual Representations from Paired Images and Text
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
stability;multi-branch network;backward propagation
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
On the Stability of Multi-branch Network
| null | null | 0 | 3.75 |
Reject
|
4;4;3;4
| null |
null |
University of Michigan, Amazon Web Services; University of Michigan; Google Cloud AI; KAIST; University of Michigan, LG AI Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2909; None
| null | 0 | null | null | null | null | null |
Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee
|
https://iclr.cc/virtual/2021/poster/2909
|
self-supervised learning;unsupervised representation learning;contrastive representation learning;data augmentation;MixUp
| null | 0 | null | null |
iclr
| 0.174078 | 0 | null |
main
| 6 |
3;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2909
|
$i$-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning
|
https://github.com/kibok90/imix
| null | 0 | 4.25 |
Poster
|
4;5;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
style transfer;text style;text generation;generative models;conditional generation
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
TextSETTR: Label-Free Text Style Extraction and Tunable Targeted Restyling
| null | null | 0 | 3 |
Reject
|
2;4;4;2
| null |
null |
Dept. of Computer Science, ETH Zurich
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3116; None
| null | 0 | null | null | null | null | null |
Núria Armengol Urpí, Sebastian Curi, Andreas Krause
|
https://iclr.cc/virtual/2021/poster/3116
|
offline;reinforcement learning;risk-averse;risk sensitive;robust;safety;safe
| null | 0 | null | null |
iclr
| -0.720577 | 0 | null |
main
| 6.4 |
5;6;6;7;8
| null |
https://iclr.cc/virtual/2021/poster/3116
|
Risk-Averse Offline Reinforcement Learning
|
https://github.com/nuria95/O-RAAC
| null | 0 | 3.4 |
Poster
|
4;4;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.5 |
4;6;6;6
| null | null |
Towards a Reliable and Robust Dialogue System for Medical Automatic Diagnosis
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;wide networks;training dynamics
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
The large learning rate phase of deep learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Explanations;Concept based explanations;Learning symbolic representations;Sequential Decision Making;Planning;Reinforcement learning.
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;5;6;7;7
| null | null |
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations
| null | null | 0 | 3.8 |
Reject
|
4;4;3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Memory Augmented Neural Networks;Continual Memory;Reasoning After Long-Term Memorization
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Continual Memory: Can We Reason After Long-Term Memorization?
| null | null | 0 | 3 |
Reject
|
2;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
natural language processing;graph neural network;graph-to-text;geographical navigation
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Generating Landmark Navigation Instructions from Maps as a Graph-to-Text Problem
| null | null | 0 | 4 |
Withdraw
|
5;4;4;3
| null |
null |
University of Verona, Department of Computer Science
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3046; None
| null | 0 | null | null | null | null | null |
Enrico Marchesini, Davide Corsi, Alessandro Farinelli
|
https://iclr.cc/virtual/2021/poster/3046
|
Deep Reinforcement Learning;Evolutionary Algorithms;Formal Verification;Machine Learning for Robotics
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3046
|
Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Language modeling;controllable generation;decoding schemes;auto-regressive models;language modeling safety
| null | 0 | null | null |
iclr
| -0.422577 | 0 | null |
main
| 5.2 |
4;5;5;6;6
| null | null |
GeDi: Generative Discriminator Guided Sequence Generation
| null | null | 0 | 4 |
Reject
|
5;4;3;4;4
| null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.