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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Federated Learning;Graph Neural Network;Spatio-Temporal Data Modeling
| null | 0 | null | null |
iclr
| -0.966092 | 0 | null |
main
| 5 |
3;5;6;6
| null | null |
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
| null | null | 0 | 2.75 |
Reject
|
5;3;2;1
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
domain adaptation;transfer learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Neural Networks;Quantization;Quantize;Pruning;MobileNet;compression
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Filter pre-pruning for improved fine-tuning of quantized deep neural networks
| null | null | 0 | 3.25 |
Reject
|
3;3;3;4
| null |
null |
KAIST; Criteo AI Lab; ENSAE ParisTech; Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2661; None
| null | 0 | null | null | null | null | null |
Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel
|
https://iclr.cc/virtual/2021/poster/2661
|
determinantal point processes;unsupervised learning;representation learning;submodular optimization
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 8 |
7;8;9
| null |
https://iclr.cc/virtual/2021/poster/2661
|
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
| null | null | 0 | 4.333333 |
Oral
|
4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
CAD;dataset;3D;reconstruction;environment;design;sequence
| null | 0 | null | null |
iclr
| -0.645497 | 0 | null |
main
| 6 |
4;5;7;8
| null | null |
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Reconstruction
| null | null | 0 | 3 |
Reject
|
4;3;4;1
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Image classification;distribution shift
| null | 0 | null | null |
iclr
| -0.942809 | 0 | null |
main
| 4 |
3;3;4;6
| null | null |
Sample Balancing for Improving Generalization under Distribution Shifts
| null | null | 0 | 4.5 |
Withdraw
|
5;5;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised disentangled representation learning;network ensemble;variational auto encoder
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;5;7
| null | null |
Improving the Unsupervised Disentangled Representation Learning with VAE Ensemble
| null | null | 0 | 3.666667 |
Reject
|
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
| 4.25 |
4;4;4;5
| null | null |
On Batch-size Selection for Stochastic Training for Graph Neural Networks
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null |
Hugging Face; Technion – Israel Institute of Technology
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3089; None
| null | 0 | null | null | null | null | null |
Victor Sanh, Thomas Wolf, Yonatan Belinkov, Alexander M Rush
|
https://iclr.cc/virtual/2021/poster/3089
|
dataset bias;product of experts;natural language processing
| null | 0 | null | null |
iclr
| -0.980196 | 0 | null |
main
| 5.5 |
2;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3089
|
Learning from others' mistakes: Avoiding dataset biases without modeling them
| null | null | 0 | 4.25 |
Poster
|
5;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
ballroom;sequence;deep;learning;machine;markov;prior
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Ballroom Dance Movement Recognition Using a Smart Watch and Representation Learning
| null | null | 0 | 4.333333 |
Reject
|
3;5;5
| null |
null |
Mila, Quebec AI Institute; School of Computer Science, McGill University; Mila, Quebec AI Institute; School of Computer Science, McGill University; Facebook AI Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2850; None
| null | 0 | null | null | null | null | null |
Wonseok Jeon, Chen-Yang Su, Paul Barde, Thang Doan, Derek Nowrouzezahrai, Joelle Pineau
|
https://iclr.cc/virtual/2021/poster/2850
|
inverse reinforcement learning;reward learning;regularized markov decision processes;reinforcement learning
| null | 0 | null | null |
iclr
| 0.218218 | 0 | null |
main
| 6.8 |
6;6;7;7;8
| null |
https://iclr.cc/virtual/2021/poster/2850
|
Regularized Inverse Reinforcement Learning
| null | null | 0 | 3.4 |
Spotlight
|
4;3;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative adversarial networks;conditional scene generation;zero-shot generalization;out of distribution
| null | 0 | null | null |
iclr
| 0.090909 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Generating unseen complex scenes: are we there yet?
| null | null | 0 | 4.25 |
Reject
|
5;4;3;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph theory;graph measures;kernel k-means;clustering
| null | 0 | null | null |
iclr
| -0.547723 | 0 | null |
main
| 4.5 |
3;4;5;6
| null | null |
Dissecting graph measures performance for node clustering in LFR parameter space
| null | null | 0 | 4 |
Reject
|
5;5;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Representation Learning;Multi-Task Learning;Meta-Learning;Graph Neural Networks
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multitask learning
| null | 0 | null | null |
iclr
| 0.301511 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Measuring and Harnessing Transference in Multi-Task Learning
| null | null | 0 | 4.5 |
Reject
|
4;5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Memory Augmentation;Model-based reinforcement learning;Latent imagination
| null | 0 | null | null |
iclr
| -0.254824 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
Robust Memory Augmentation by Constrained Latent Imagination
| null | null | 0 | 3.75 |
Withdraw
|
4;3;5;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.688247 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
Leveraging affinity cycle consistency to isolate factors of variation in learned representations
| 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 |
memorization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Provable Memorization via Deep Neural Networks using Sub-linear Parameters
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Federated Learning;Gradient Aggregation;Variational Inference
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 4 |
3;3;4;6
| null | null |
Federated Learning with Decoupled Probabilistic-Weighted Gradient Aggregation
| null | null | 0 | 3.5 |
Reject
|
3;3;4;4
| null |
null |
Bosch Center for Artificial Intelligence, Renningen, Germany; University of T ¨ubingen, MPI for Intelligent Systems, T¨ubingen, Germany; Bosch Center for Artificial Intelligence, Renningen, Germany; University of T ¨ubingen, Germany
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2936; None
| null | 0 | null | null | null | null | null |
Katharina Ott, Prateek Katiyar, Philipp Hennig, Michael Tiemann
|
https://iclr.cc/virtual/2021/poster/2936
| null | null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/2936
|
ResNet After All: Neural ODEs and Their Numerical Solution
|
https://github.com/boschresearch/numerics_independent_neural_odes
| null | 0 | 3.75 |
Poster
|
4;4;4;3
| null |
null |
Computer Vision Lab, Delft University of Technology, Delft, Netherlands
|
2021
| 0 | null | null | 0 | null | null | null | null | null |
AAAI Press Staff, Pater Patel Schneider, Sunil Issar, J. Scott Penberthy, George Ferguson, Hans Guesgen, Francisco Cruz, Marc Pujol-Gonzalez
| null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Less bits is more: How pruning deep binary networks increases weight capacity
|
https://github.com/liyunqianggyn/Equal-Bits-BNN
| null | 0 | 0 |
Withdraw
| null | null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural Language Processing;Lottery Tickets Hypothesis;Efficient Training
| null | 0 | null | null |
iclr
| -0.492366 | 0 | null |
main
| 5.2 |
3;5;5;6;7
| null | null |
EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets
| 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 | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;5;6
| null | null |
Wasserstein Distributionally Robust Optimization: A Three-Player Game Framework
| null | null | 0 | 3.2 |
Reject
|
3;3;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Calibration;Reliable Uncertainty Quantification;Probabilistic Deep Learning
| null | 0 | null | null |
iclr
| 0.57735 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Quantile Regularization : Towards Implicit Calibration of Regression Models
| null | null | 0 | 3.5 |
Reject
|
3;3;4;4
| null |
null |
Max-Planck Institute, Tübingen, Germany; ETH Zurich, Zürich, Switzerland; Swiss AI Lab IDSIA, USI, Lugano, Switzerland
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3133; None
| null | 0 | null | null | null | null | null |
Đorđe Miladinović, Aleksandar Stanić, Stefan Bauer, Jürgen Schmidhuber, Joachim M Buhmann
|
https://iclr.cc/virtual/2021/poster/3133
|
Neural networks;Deep generative models;Image Modeling;Variational Autoencoders
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 6.5 |
6;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3133
|
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling
|
https://github.com/djordjemila/sdn
| null | 0 | 3.5 |
Poster
|
3;5;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;non-adversarial robustness;sensitivity;input perturbation;contextual feature utility;contextual feature sensitivity.
| null | 0 | null | null |
iclr
| 0.454545 | 0 | null |
main
| 5.75 |
5;5;6;7
| null | null |
Balancing Robustness and Sensitivity using Feature Contrastive Learning
| null | null | 0 | 3.25 |
Reject
|
4;2;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Flatness is a False Friend
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Robust Control;Reinforcement Learning;Multiagent Systems
| null | 0 | null | null |
iclr
| 0.132453 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
Robust Reinforcement Learning using Adversarial Populations
| 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 |
Partial differential equation;nonlinear equation;Newton-Raphson method;convolutional neural network
| null | 0 | null | null |
iclr
| 0.080064 | 0 | null |
main
| 5.4 |
4;5;5;6;7
| null | null |
Learning to Solve Nonlinear Partial Differential Equation Systems To Accelerate MOSFET Simulation
| null | null | 0 | 3.8 |
Reject
|
4;2;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
defense framework;widespread adversarial examples;perturbation-invariant representation;adversarial learning
| null | 0 | null | null |
iclr
| -0.420084 | 0 | null |
main
| 4.5 |
2;3;6;7
| null | null |
ADD-Defense: Towards Defending Widespread Adversarial Examples via Perturbation-Invariant Representation
| null | null | 0 | 4.75 |
Withdraw
|
5;5;4;5
| null |
null |
Stanford University; Carnegie Mellon University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3093; None
| null | 0 | null | null | null | null | null |
Benedikt Boecking, Willie Neiswanger, Eric P Xing, Artur Dubrawski
|
https://iclr.cc/virtual/2021/poster/3093
|
weak supervision;data programming;data labeling;active learning
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 6.5 |
6;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/3093
|
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
| null | null | 0 | 3.75 |
Poster
|
3;4;4;4
| null |
null |
School of Informatics, University of Edinburgh; CSML, Istituto Italiano di Tecnologia & Department of Computer Science, UCL
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3044; None
| null | 0 | null | null | null | null | null |
Henry Gouk, Timothy Hospedales, massimiliano pontil
|
https://iclr.cc/virtual/2021/poster/3044
|
Deep Learning;Transfer Learning;Statistical Learning Theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3044
|
Distance-Based Regularisation of Deep Networks for Fine-Tuning
| null | null | 0 | 4 |
Poster
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
contrastive learning;self-supervised learning;representation learning;theory
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Contrastive estimation reveals topic posterior information to linear models
| null | null | 0 | 3.25 |
Reject
|
4;2;3;4
| null |
null |
Under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Explanability;contrastive explanations;duality
| null | 0 | null | null |
iclr
| -0.166667 | 0 | null |
main
| 6 |
5;5;6;8
| null | null |
On Relating "Why?" and "Why Not?" Explanations
| null | null | 0 | 3 |
Reject
|
2;5;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Information Bottleneck;Reinforcement Learning;Stein Variational Gradient
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Optimizing Information Bottleneck in Reinforcement Learning: A Stein Variational Approach
| null | null | 0 | 4 |
Withdraw
|
4;4;3;5
| null |
null |
Département d’Informatique et de Recherche Opérationnelle, Université de Montréal, Mila, CIFAR fellow; Département d’Informatique et de Recherche Opérationnelle, Université de Montréal, Mila
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2614; None
| null | 0 | null | null | null | null | null |
Samuel Lavoie, Faruk Ahmed, Aaron Courville
|
https://iclr.cc/virtual/2021/poster/2614
|
Unsupervised Domain Translation;Unsupervised Learning;Image-to-Image Translation;Deep Learning;Representation Learning
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 6.25 |
4;7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2614
|
Integrating Categorical Semantics into Unsupervised Domain Translation
|
https://github.com/lavoiems/Cats-UDT
| null | 0 | 3.25 |
Poster
|
4;2;4;3
| null |
null |
Department of Statistical Science, University College London; SenseTime Research/Qing Yuan Research Institute, Shanghai Jiao Tong University; Shenzhen International Graduate School/Department of Electronic Engineering, Tsinghua University; SenseTime Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2894; None
| null | 0 | null | null | null | null | null |
Liyang Liu, Yi Li, Zhanghui Kuang, Jing-Hao Xue, Yimin Chen, Wenming Yang, Qingmin Liao, Wei Zhang
|
https://iclr.cc/virtual/2021/poster/2894
|
Multi-task Learning;Impartial Learning;Scene Understanding
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null |
https://iclr.cc/virtual/2021/poster/2894
|
Towards Impartial Multi-task Learning
| null | null | 0 | 4.666667 |
Poster
|
5;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised learning;clustering;manifold separation;representation learning;Bayes rule
| null | 0 | null | null |
iclr
| 0.707107 | 0 | null |
main
| 4.5 |
4;4;5;5
| null | null |
Neural Bayes: A Generic Parameterization Method for Unsupervised Learning
| null | null | 0 | 4 |
Reject
|
3;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep reinforcement learning;transfer learning;unsupervised learning;exploration
| null | 0 | null | null |
iclr
| -0.968496 | 0 | null |
main
| 5.25 |
4;4;5;8
| null | null |
Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning
| 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 |
continual learning;incremental learning;causal effect inference;representation learning;treatment effect estimation
| null | 0 | null | null |
iclr
| 0.522233 | 0 | null |
main
| 3.25 |
2;3;4;4
| null | null |
Continual Lifelong Causal Effect Inference with Real World Evidence
| null | null | 0 | 4.25 |
Reject
|
4;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Architecture Search;AutoML;Reinforcement Learning (RL)
| null | 0 | null | null |
iclr
| -0.301511 | 0 | null |
main
| 5.5 |
5;5;6;6
| null | null |
Fast MNAS: Uncertainty-aware Neural Architecture Search with Lifelong Learning
| 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 |
Metagenomics;Deep Learning;End-to-End machine learning;Multiple Instance Learning;Precision Medicine;Disease Prediction;attention mechanism
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Towards end-to-end disease prediction from raw metagenomic data
| null | null | 0 | 0 |
Desk Reject
| null | null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Layout Estimation;Deep Stereo;Computer Vision
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
SBEVNet: End-to-End Deep Stereo Layout Estimation
| 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 |
Adversarial Machine Learning;Model Ensemble;Certified Robustness
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
On the Certified Robustness for Ensemble Models and Beyond
| null | null | 0 | 3.5 |
Reject
|
4;2;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised learning;object-centric representations;benchmark;tracking
| null | 0 | null | null |
iclr
| 0.760886 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
Benchmarking Unsupervised Object Representations for Video Sequences
| null | null | 0 | 2.25 |
Reject
|
1;2;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
treatment effect;interpretability;healthcare;causal inference
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.4 |
3;4;4;7;9
| null | null |
SyncTwin: Transparent Treatment Effect Estimation under Temporal Confounding
| null | null | 0 | 4 |
Reject
|
4;3;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta-learning;deep learning
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 5.75 |
5;6;6;6
| null | null |
Variable-Shot Adaptation for Online Meta-Learning
| null | null | 0 | 3.75 |
Reject
|
4;3;4;4
| null |
null |
DeepMind
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3029; None
| null | 0 | null | null | null | null | null |
Andrew Brock, Soham De, Samuel Smith
|
https://iclr.cc/virtual/2021/poster/3029
|
normalizers;signal propagation;deep learning;neural networks;ResNets;EfficientNets;ImageNet;CNNs;ConvNets
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.333333 |
5;7;7
| null |
https://iclr.cc/virtual/2021/poster/3029
|
Characterizing signal propagation to close the performance gap in unnormalized ResNets
|
http://dpmd.ai/nfnets
| null | 0 | 4 |
Poster
|
5;3;4
| null |
null |
Under double-blind review
|
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
causal discovery
| null | 0 | null | null |
iclr
| -0.816497 | 0 | null |
main
| 4.25 |
4;4;4;5
| null | null |
Hokey Pokey Causal Discovery: Using Deep Learning Model Errors to Learn Causal Structure
| null | null | 0 | 4 |
Withdraw
|
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
| -0.688247 | 0 | null |
main
| 5.75 |
4;6;6;7
| null | null |
Quickly Finding a Benign Region via Heavy Ball Momentum in Non-Convex Optimization
| null | null | 0 | 3.5 |
Reject
|
4;3;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
machine translation;Referential Problem;low-resource
| null | 0 | null | null |
iclr
| -0.636364 | 0 | null |
main
| 3.25 |
2;3;4;4
| null | null |
A Simple and General Strategy for Referential Problem in Low-Resource Neural Machine Translation
| 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 |
reinforcement learning;hierarchical methods;formal methods;formal logic
| null | 0 | null | null |
iclr
| -0.688247 | 0 | null |
main
| 5 |
4;4;6;6
| null | null |
The Logical Options Framework
| null | null | 0 | 3.75 |
Reject
|
5;4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Conditional independence;f-divergence;rotation invariant;neural network;statistical guarantee
| null | 0 | null | null |
iclr
| -0.774597 | 0 | null |
main
| 5.5 |
4;5;6;7
| null | null |
Sufficient and Disentangled Representation 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 |
Neural Rendering;Reshading;Relighting;Computational Photography;Image Decomposition
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Cut-and-Paste Neural Rendering
| null | null | 0 | 3.333333 |
Reject
|
4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial defense;semantic segmentation;robustness
| null | 0 | null | null |
iclr
| -0.4842 | 0 | null |
main
| 4.75 |
3;5;5;6
| null | null |
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation
| null | null | 0 | 3.25 |
Withdraw
|
4;4;2;3
| null |
null |
MIT CSAIL; Element AI; University of Toronto, Vector Institute
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3159; None
| null | 0 | null | null | null | null | null |
Jonathan Frankle, Gintare Dziugaite, Anonymous A Author, Michael Carbin
|
https://iclr.cc/virtual/2021/poster/3159
|
Pruning;Sparsity;Lottery Ticket;Science
| null | 0 | null | null |
iclr
| 0.250873 | 0 | null |
main
| 6.5 |
4;6;7;9
| null |
https://iclr.cc/virtual/2021/poster/3159
|
Pruning Neural Networks at Initialization: Why Are We Missing the Mark?
| null | null | 0 | 4.25 |
Poster
|
4;5;3;5
| null |
null |
NVIDIA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3204; None
| null | 0 | null | null | null | null | null |
Rafael Valle, Kevin J Shih, Ryan Prenger, Bryan Catanzaro
|
https://iclr.cc/virtual/2021/poster/3204
|
Text to speech synthesis;normalizing flows;deep learning
| null | 0 | null | null |
iclr
| 0.666667 | 0 | null |
main
| 6.5 |
5;6;6;9
| null |
https://iclr.cc/virtual/2021/poster/3204
|
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
|
https://github.com/NVIDIA/flowtron
| null | 0 | 4 |
Poster
|
3;3;5;5
| null |
null |
Georgia Institute of Technology
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3296; None
| null | 0 | null | null | null | null | null |
Manas Sahni, Shreya Varshini, Alind Khare, Alexey Tumanov
|
https://iclr.cc/virtual/2021/poster/3296
|
Efficient Deep Learning;Latency-aware Neural Architecture Search;AutoML
| null | 0 | null | null |
iclr
| 0.13484 | 0 | null |
main
| 5.5 |
4;5;6;7
| null |
https://iclr.cc/virtual/2021/poster/3296
|
CompOFA – Compound Once-For-All Networks for Faster Multi-Platform Deployment
|
https://github.com/gatech-sysml/CompOFA
| null | 0 | 3.25 |
Poster
|
4;2;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
inherited classifier;embedding space alignment;face recognition;knowledge distillation
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
ProxylessKD: Direct Knowledge Distillation with Inherited Classifier for Face Recognition
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph neural network;neural architecture search;automated machine learning
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 4.5 |
3;5;5;5
| null | null |
Efficient Graph Neural Architecture Search
| null | null | 0 | 4.25 |
Reject
|
5;3;5;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Network Pruning;Channel pruning;Spatial pruning;Network Compression;MIQCQP;Specified target resource constraint
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;5;7;7
| null | null |
Succinct Network Channel and Spatial Pruning via Discrete Variable QCQP
| null | null | 0 | 3.5 |
Reject
|
2;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
explainable reinforcement learning;hierarchical reinforcement learning;goal-based interpretability
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 3.25 |
3;3;3;4
| null | null |
Explainable Reinforcement Learning Through Goal-Based Interpretability
| null | null | 0 | 4.25 |
Reject
|
4;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
certified defense;watermarking;backdoor attack
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Certified Watermarks for Neural Networks
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Federated learning;User verification models
| null | 0 | null | null |
iclr
| -0.15523 | 0 | null |
main
| 5.75 |
2;6;7;8
| null | null |
Secure Federated Learning of User Verification Models
| 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
| -0.816497 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Everybody's Talkin': Let Me Talk as You Want
| 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 |
Hyperparameter optimization;Meta-learning
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
Non-greedy Gradient-based Hyperparameter Optimization Over Long Horizons
| null | null | 0 | 2.5 |
Reject
|
2;4;2;2
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Optimization Algorithm;Adaptive algorithms;Online Learning;Regret Minimization
| null | 0 | null | null |
iclr
| -0.731925 | 0 | null |
main
| 5 |
3;4;5;5;8
| null | null |
On the Marginal Regret Bound Minimization of Adaptive Methods
| 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 |
Knowledge Distillation;Sparse Representation;Transfer Learning
| null | 0 | null | null |
iclr
| -0.090909 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Knowledge Distillation By Sparse Representation Matching
| 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 |
over-smoothing
| null | 0 | null | null |
iclr
| 0.816497 | 0 | null |
main
| 4.5 |
3;5;5;5
| null | null |
Teleport Graph Convolutional Networks
| null | null | 0 | 4 |
Reject
|
3;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Birkhoff-von-Neumann decomposition;doubly stochastic matrices;Riemannian optimization;Fairness exposure in ranking
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Approximate Birkhoff-von-Neumann decomposition: a differentiable approach
| null | null | 0 | 2.333333 |
Reject
|
1;3;3
| 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
| 4.25 |
4;4;4;5
| null | null |
Fair Differential Privacy Can Mitigate the Disparate Impact on Model Accuracy
| null | null | 0 | 3.5 |
Reject
|
3;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Representations Learning;Few-shot Learning;Contrastive Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5;5
| null | null |
Function Contrastive Learning of Transferable Representations
| null | null | 0 | 3.5 |
Reject
|
3;3;4;4
| null |
null |
Lund University; Lund University and Google Research
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3324; None
| null | 0 | null | null | null | null | null |
Martin Trimmel, Henning Petzka, Cristian Sminchisescu
|
https://iclr.cc/virtual/2021/poster/3324
|
linear regions;linear terms;deep learning theory;deep neural networks;rectified linear unit;relu network;piecewise linear function;tropical function
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
6;6;6;8
| null |
https://iclr.cc/virtual/2021/poster/3324
|
TropEx: An Algorithm for Extracting Linear Terms in Deep Neural Networks
| null | null | 0 | 3 |
Poster
|
3;3;3;3
| null |
null |
DeepMind, London, UK
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3187; None
| null | 0 | null | null | null | null | null |
Jessica Hamrick, Abram Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Buesing, Petar Veličković, Theophane Weber
|
https://iclr.cc/virtual/2021/poster/3187
|
model-based RL;planning;MuZero
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 6.25 |
5;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3187
|
On the role of planning in model-based deep reinforcement learning
| 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 |
Stochastic optimization
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 3.75 |
3;3;4;5
| null | null |
Stochastic Optimization with Non-stationary Noise: The Power of Moment Estimation
| null | null | 0 | 4.5 |
Reject
|
5;5;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Depth-from-defocus;Depth-from-focus;Unsupervised learning
| null | 0 | null | null |
iclr
| -0.899229 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
Unsupervised Simultaneous Depth-from-defocus and Depth-from-focus
| null | null | 0 | 4.25 |
Withdraw
|
5;5;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph embedding;Theory;Topology;Functional analysis
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 2.25 |
2;2;2;3
| null | null |
$Graph Embedding via Topology and Functional Analysis$
| null | null | 0 | 4.5 |
Withdraw
|
5;5;5;3
| null |
null |
Department of Computer Science, Stanford University, Stanford, CA 94305; School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3070; None
| null | 0 | null | null | null | null | null |
Ioannis Exarchos, Marcus A Pereira, Ziyi Wang, Evangelos Theodorou
|
https://iclr.cc/virtual/2021/poster/3070
|
deep neural networks;nested optimization;stochastic control;deep FBSDEs
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 5.5 |
4;6;6;6
| null |
https://iclr.cc/virtual/2021/poster/3070
|
NOVAS: Non-convex Optimization via Adaptive Stochastic Search for End-to-end Learning and Control
| null | null | 0 | 2.25 |
Poster
|
2;3;2;2
| null |
null |
Naver AI Lab, Naver Clova; Applied Information Engineering, Yonsei University; Naver AI Lab
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2880; None
| null | 0 | null | null | null | null | null |
Byeongho Heo, Sanghyuk Chun, Seong Joon Oh, Dongyoon Han, Sangdoo Yun, Gyuwan Kim, Youngjung Uh, Jung-Woo Ha
|
https://iclr.cc/virtual/2021/poster/2880
|
momentum optimizer;scale-invariant weights;normalize layer;effective learning rate
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/2880
|
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights
|
https://github.com/clovaai/adamp
| null | 0 | 3.5 |
Poster
|
4;4;2;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Convolutional neural network;spatio-temporal forecasting;data-driven physics;wave dynamics
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.75 |
3;4;5;7
| null | null |
Fully Convolutional Approach for Simulating Wave Dynamics
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3232; None
| null | 0 | null | null | null | null | null |
Hsiang-Yun Sherry Chien, Jinhan Zhang, Christopher Honey
|
https://iclr.cc/virtual/2021/poster/3232
|
natural language processing;LSTM;timescale;hierarchy;temporal context
| null | 0 | null | null |
iclr
| -0.57735 | 0 | null |
main
| 5.5 |
3;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3232
|
Mapping the Timescale Organization of Neural Language Models
|
https://github.com/sherrychien/LSTM_timescales
| null | 0 | 3.75 |
Poster
|
4;4;4;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Architecture Search;AutoML;Neural Networks
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.75 |
5;5;5;8
| null | null |
Exploring single-path Architecture Search ranking correlations
| null | null | 0 | 4.25 |
Reject
|
4;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep double descent;feedforward neural network;image classificaiton
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.5 |
2;3;4;5
| null | null |
Mitigating Deep Double Descent by Concatenating Inputs
| null | null | 0 | 3.5 |
Reject
|
4;3;3;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
latent variable modelling;lfads;neuroscience;variational autoencoders;dynamical systems;calcium imaging;neural data analysis
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
CaLFADS: latent factor analysis of dynamical systems in calcium imaging data
| 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;computational efficiency;memory efficiency
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay
| null | null | 0 | 4 |
Reject
|
5;4;3;4
| null |
null |
Northeastern University; University of California, San Diego
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3189; None
| null | 0 | null | null | null | null | null |
Robin Walters, Jinxi Li, Rose Yu
|
https://iclr.cc/virtual/2021/poster/3189
|
equivariant;symmetry;trajectory prediction;continuous convolution;argoverse
| null | 0 | null | null |
iclr
| 0.426401 | 0 | null |
main
| 6 |
5;6;6;7
| null |
https://iclr.cc/virtual/2021/poster/3189
|
Trajectory Prediction using Equivariant Continuous Convolution
|
https://github.com/Rose-STL-Lab/ECCO
| null | 0 | 2.75 |
Poster
|
2;4;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
text generation;text degeneration;language model;summarization;image captioning
| null | 0 | null | null |
iclr
| -0.426401 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Straight to the Gradient: Learning to Use Novel Tokens for Neural Text Generation
| null | null | 0 | 4 |
Reject
|
4;5;4;3
| null |
null |
Department of Statistics, University of Michigan; IBM Research, MIT-IBM Watson AI Lab; Department of Mathematics, University of Michigan; School of Information Science and Technology, ShanghaiTech University
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2749; None
| null | 0 | null | null | null | null | null |
Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun
|
https://iclr.cc/virtual/2021/poster/2749
|
Algorithmic fairness;boosting;non-smooth models
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null |
https://iclr.cc/virtual/2021/poster/2749
|
Individually Fair Gradient Boosting
| null | null | 0 | 3.333333 |
Spotlight
|
4;4;2
| null |
null |
Department of Computer Science, University of Maryland, College Park, MD 20740, USA
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/2548; None
| null | 0 | null | null | null | null | null |
Sahil Singla, Soheil Feizi
|
https://iclr.cc/virtual/2021/poster/2548
|
spectral regularization;spectral normalization
| null | 0 | null | null |
iclr
| 0.92582 | 0 | null |
main
| 5 |
3;4;5;8
| null |
https://iclr.cc/virtual/2021/poster/2548
|
Fantastic Four: Differentiable and Efficient Bounds on Singular Values of Convolution Layers
| null | null | 0 | 4.25 |
Poster
|
4;4;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph Pooling;Graph Classiciation;Interaction Preserving Graph Pooling;Structure Landmarking
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6;6
| null | null |
Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification
| null | null | 0 | 3.25 |
Reject
|
4;4;2;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural Language Processing;Text Classification;Information Geomtery;Sentiment Analysis
| null | 0 | null | null |
iclr
| -0.4842 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Geometry matters: Exploring language examples at the decision boundary
| null | null | 0 | 3.75 |
Reject
|
4;5;2;4
| null |
null |
Department of Mathematics, ETH Zurich, Switzerland
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3339; None
| null | 0 | null | null | null | null | null |
Calypso Herrera, Florian Krach, Josef Teichmann
|
https://iclr.cc/virtual/2021/poster/3339
|
Neural ODE;conditional expectation;irregular-observed data modelling
| null | 0 | null | null |
iclr
| 0.182574 | 0 | null |
main
| 6 |
4;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3339
|
Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering
| null | null | 0 | 3.5 |
Poster
|
3;4;5;2
| null |
null |
KAIST, AITRICS, South Korea
|
2021
| 0 |
https://iclr.cc/virtual/2021/poster/3311; None
| null | 0 | null | null | null | null | null |
Jinheon Baek, Minki Kang, Sung Ju Hwang
|
https://iclr.cc/virtual/2021/poster/3311
|
Graph representation learning;Graph pooling
| null | 0 | null | null |
iclr
| 0.408248 | 0 | null |
main
| 6 |
4;6;7;7
| null |
https://iclr.cc/virtual/2021/poster/3311
|
Accurate Learning of Graph Representations with Graph Multiset Pooling
|
https://github.com/JinheonBaek/GMT
| null | 0 | 4.5 |
Poster
|
4;5;4;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
ML for Chemistry;Polymer Retrosynthesis;Few-show Learning;Domain Adaptation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;6;7
| null | null |
PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation
| null | null | 0 | 3 |
Reject
|
3;3;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Imitation;Reinforcement Learning;Free Energy Principle
| null | 0 | null | null |
iclr
| 0.272166 | 0 | null |
main
| 5 |
4;5;5;6
| null | null |
Combining Imitation and Reinforcement Learning with Free Energy Principle
| null | null | 0 | 3.25 |
Reject
|
4;2;2;5
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Fixed-point;Attention;Feed Forward Network;Transformer;Recurrent Neural Network;Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
Transforming Recurrent Neural Networks with Attention and Fixed-point Equations
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Automated machine learning;zero-shot learning;graph neural networks;transformers
| null | 0 | null | null |
iclr
| -0.612372 | 0 | null |
main
| 3.6 |
2;4;4;4;4
| null | null |
Real-Time AutoML
| 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 |
adversarial examples;robust representations;feature binding
| null | 0 | null | null |
iclr
| -0.174078 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
Hierarchical Binding in Convolutional Neural Networks Confers Adversarial Robustness
| null | null | 0 | 3.25 |
Withdraw
|
3;4;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Mutual Information;Self-supervised learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Decomposing Mutual Information for Representation Learning
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null | null |
2021
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning for recommender system;optimization for representation learning;variational auto-encoder;topic modeling
| null | 0 | null | null |
iclr
| 0.454545 | 0 | null |
main
| 4.25 |
3;4;5;5
| null | null |
DeepLTRS: A Deep Latent Recommender System based on User Ratings and Reviews
| null | null | 0 | 3.75 |
Withdraw
|
3;4;3;5
| null |
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