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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
IBM Research AI, Yorktown Heights, NY, 10598, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Luis Lastras
|
https://iclr.cc/virtual/2019/poster/1056
|
latent variable modeling;rate-distortion theory;log likelihood bounds
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Information Theoretic lower bounds on negative log likelihood
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Models;GANs;Denosing;Demixing;Structured Recovery
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
LEARNING GENERATIVE MODELS FOR DEMIXING OF STRUCTURED SIGNALS FROM THEIR SUPERPOSITION USING GANS
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
quantifier;evaluation methodology;psycholinguistics;visual question answering
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
The meaning of "most" for visual question answering models
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Stanford University; University of Amsterdam; UC Berkeley
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Rohin Shah, Dmitrii Krasheninnikov, Jordan Alexander, Pieter Abbeel, Anca Dragan
|
https://iclr.cc/virtual/2019/poster/1092
|
Preference learning;Inverse reinforcement learning;Inverse optimal stochastic control;Maximum entropy reinforcement learning;Apprenticeship learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
6;6;7;7
| null | null |
Preferences Implicit in the State of the World
|
https://github.com/HumanCompatibleAI/rlsp
| null | 0 | 3.5 |
Poster
|
3;4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
transfer learning;semantic representation;spoken language understanding
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Transferring SLU Models in Novel Domains
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
CentraleSupélec, GIPSA-Lab University of GrenobleAlpes; CEA List; CEA List, CentraleSupélec
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Mohamed El Amine Seddik, mohamed Tamaazousti, Romain Couillet
|
https://iclr.cc/virtual/2019/poster/785
|
Random Matrix Theory;Concentration of Measure;Sparse PCA;Covariance Thresholding
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 6 |
5;6;7
| null | null |
A Kernel Random Matrix-Based Approach for Sparse PCA
| null | null | 0 | 3.666667 |
Poster
|
5;4;2
| null |
null |
Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
|
https://iclr.cc/virtual/2019/poster/670
|
bayesian inference;segmentation;anticipation;multi-modality
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods
| null | null | 0 | 3.333333 |
Poster
|
2;4;4
| null |
null |
Cornell University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Ben Athiwaratkun, Marc A Finzi, Pavel Izmailov, Andrew G Wilson
|
https://iclr.cc/virtual/2019/poster/903
|
semi-supervised learning;computer vision;classification;consistency regularization;flatness;weight averaging;stochastic weight averaging
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
| null | null | 0 | 3 |
Poster
|
1;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 3 |
2;3;4
| null | null |
ATTENTIVE EXPLAINABILITY FOR PATIENT TEMPORAL EMBEDDING
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Clova AI Research, NAVER Corp.
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Sang-Woo Lee, Tong Gao, Sohee Yang, Jaejun Yoo, Jung-Woo Ha
|
https://iclr.cc/virtual/2019/poster/1039
| null | null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation
| null | null | 0 | 3.666667 |
Poster
|
2;4;5
| null |
null |
Uber Advanced Technologies Group, University of Toronto; Uber Advanced Technologies Group, University of Waterloo
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Chris Zhang, Mengye Ren, Raquel Urtasun
|
https://iclr.cc/virtual/2019/poster/740
|
neural;architecture;search;graph;network;hypernetwork;meta;learning;anytime;prediction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Graph HyperNetworks for Neural Architecture Search
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative adversarial networks;generative models;clustering;visual object recognition
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;6
| null | null |
Multi-Modal Generative Adversarial Networks for Diverse Datasets
| null | null | 0 | 4 |
Withdraw
|
4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Referential Language;3D Objects;Part-Awareness;Neural Speakers;Neural Listeners
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Learning to Refer to 3D Objects with Natural Language
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
University of Illinois at Urbana-Champaign; Big Data Department, Baidu Inc.; Big Data Lab, Baidu Research
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Xingjian Li, Haoyi Xiong, Hanchao Wang, Yuxuan Rao, Liping Liu, Luke Huan
|
https://iclr.cc/virtual/2019/poster/644
|
transfer learning;deep learning;regularization;attention;cnn
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
DELTA: DEEP LEARNING TRANSFER USING FEATURE MAP WITH ATTENTION FOR CONVOLUTIONAL NETWORKS
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adaptive gradient descent;deeplearning;ADAM;RMSProp;autoencoders
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Convergence Guarantees for RMSProp and ADAM in Non-Convex Optimization and an Empirical Comparison to Nesterov Acceleration
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Do Language Models Have Common Sense?
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Generative Model;VAE;log hyperbolic cosine loss
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Log Hyperbolic Cosine Loss Improves Variational Auto-Encoder
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Corporate Technology, Machine-Intelligence (MIC-DE), Siemens AG Munich, Germany; CIS, University of Munich (LMU) Munich, Germany
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schuetze
|
https://iclr.cc/virtual/2019/poster/1069
|
neural topic model;natural language processing;text representation;language modeling;information retrieval;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
textTOvec: DEEP CONTEXTUALIZED NEURAL AUTOREGRESSIVE TOPIC MODELS OF LANGUAGE WITH DISTRIBUTED COMPOSITIONAL PRIOR
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
computer vision;out-of-distribution detection;image classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Detecting Out-Of-Distribution Samples Using Low-Order Deep Features Statistics
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Princeton University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Sachin Ravi, Alex Beatson
|
https://iclr.cc/virtual/2019/poster/940
|
variational inference;meta-learning;few-shot learning;uncertainty quantification
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Amortized Bayesian Meta-Learning
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Inverse reinforcement learning;differentiable planning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Inferring Reward Functions from Demonstrators with Unknown Biases
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Dense graph propagation;zero-shot learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Rethinking Knowledge Graph Propagation for Zero-Shot Learning
| null | null | 0 | 3.666667 |
Withdraw
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Variational autoencoder;Unsupervised learning;(Semi-)Supervised learning;Topic modeling
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Dirichlet Variational Autoencoder
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
uncertainty in neural networks;ensemble;mixture model
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Compound Density Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
autoencoder;generative models;deep neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Cramer-Wold AutoEncoder
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Towards Decomposed Linguistic Representation with Holographic Reduced Representation
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Artificial Intelligence;Deep learning;Machine learning;Compression
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
NETWORK COMPRESSION USING CORRELATION ANALYSIS OF LAYER RESPONSES
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
online learning;nonconvex optimization;robust optimization
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 5.25 |
4;5;6;6
| null | null |
Optimal Attacks against Multiple Classifiers
| null | null | 0 | 3.75 |
Reject
|
4;4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Dual Importance Weight GAN
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Delft University of Technology; University College London
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Ying Wen, Yaodong Yang, Rui Luo, Jun Wang, Wei Pan
|
https://iclr.cc/virtual/2019/poster/653
|
Multi-agent Reinforcement Learning;Recursive Reasoning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Neural Network Compression;Low Rank Approximation;Higher Order Tensor Decomposition
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Exploiting Invariant Structures for Compression in Neural Networks
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
derivative-free optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
SHE2: Stochastic Hamiltonian Exploration and Exploitation for Derivative-Free Optimization
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
interpretability;sequence labeling;named entity recognition;LSTM;attention
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Understanding and Improving Sequence-Labeling NER with Self-Attentive LSTMs
| null | null | 0 | 4.333333 |
Withdraw
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
interpretability;rationalization;text matching;dependent selection
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Learning Corresponded Rationales for Text Matching
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Riemannian geometry;Python package;machine learning;deep learning
| null | 0 | null | null |
iclr
| -0.956689 | 0 |
https://goo.gl/XV2Rb7
|
main
| 4.75 |
3;4;4;8
| null | null |
Geomstats: a Python Package for Riemannian Geometry in Machine Learning
| null | null | 0 | 4 |
Reject
|
5;5;4;2
| null |
null |
Stanford; Siemens
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon
|
https://iclr.cc/virtual/2019/poster/948
|
Partial differential equation;deep learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Learning Neural PDE Solvers with Convergence Guarantees
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Noisy Labels;Adversarial Attacks;Generative Models
| null | 0 | null | null |
iclr
| 0.970725 | 0 | null |
main
| 4.666667 |
3;4;7
| null | null |
Robust Determinantal Generative Classifier for Noisy Labels and Adversarial Attacks
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
imitation learning;state-only observations;self-exploration
| null | 0 | null | null |
iclr
| -0.654654 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Reinforced Imitation Learning from Observations
| null | null | 0 | 3.666667 |
Reject
|
4;5;2
| null |
null |
Georgia Institute of Technology; Google; University of British Columbia
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Weiwei Kong, Christopher Liaw, Aranyak Mehta, D. Sivakumar
|
https://iclr.cc/virtual/2019/poster/1034
|
reinforcement learning;algorithms;adwords;knapsack;secretary
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
A new dog learns old tricks: RL finds classic optimization algorithms
| null | null | 0 | 3.666667 |
Poster
|
3;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
privacy-preserving;image classification;adversarial training;learnable obfuscator
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
A PRIVACY-PRESERVING IMAGE CLASSIFICATION FRAMEWORK WITH A LEARNABLE OBFUSCATOR
| null | null | 0 | 4.333333 |
Withdraw
|
5;4;4
| null |
null | null |
2019
| 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 |
Pushing the bounds of dropout
| null | null | 0 | 2.666667 |
Reject
|
3;2;3
| null |
null |
Mila – Québec Artificial Intelligence Institute, Google Brain; Microsoft Research, Mila – Québec Artificial Intelligence Institute; Mila – Québec Artificial Intelligence Institute, McGill University; Department of Computer Science and Technology, University of Cambridge; Mila – Québec Artificial Intelligence Institute, Université de Montréal
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Petar Veličković, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm
|
https://iclr.cc/virtual/2019/poster/782
|
Unsupervised Learning;Graph Neural Networks;Graph Convolutions;Mutual Information;Infomax;Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
5;7;9
| null | null |
Deep Graph Infomax
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Negative Sampling;Sampled Softmax;Word embeddings;Adversarial Networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Partially Mutual Exclusive Softmax for Positive and Unlabeled data
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
state estimation;recurrent neural networks;Kalman Filter;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
learning from observations;safe reinforcement learning;deep reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Safe Policy Learning from Observations
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Data parallel;Deep Learning;Multiple GPU system;Communication Compression;Sparsification;Quantization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
RedSync : Reducing Synchronization Traffic for Distributed Deep Learning
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Princeton University and Institute for Advanced Study; Tsinghus University; Princeton University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Sanjeev Arora, Zhiyuan Li, Kaifeng Lyu
|
https://iclr.cc/virtual/2019/poster/960
|
batch normalization;scale invariance;learning rate;stationary point
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Theoretical Analysis of Auto Rate-Tuning by Batch Normalization
| null | null | 0 | 2.666667 |
Poster
|
4;2;2
| null |
null |
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Illinois, IL 61801, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Charbel Sakr, Naresh Shanbhag
|
https://iclr.cc/virtual/2019/poster/747
|
deep learning;reduced precision;fixed-point;quantization;back-propagation algorithm
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 6 |
3;7;8
| null | null |
Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm
| null | null | 0 | 3 |
Poster
|
2;3;4
| null |
null |
Tsinghua University; University of Toronto, Vector Institute
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Shengyang Sun, Guodong Zhang, Jiaxin Shi, Roger Grosse
|
https://iclr.cc/virtual/2019/poster/1035
|
functional variational inference;Bayesian neural networks;stochastic processes
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
FUNCTIONAL VARIATIONAL BAYESIAN NEURAL NETWORKS
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
SenseTime
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Sirui Xie, Junning Huang, Lanxin Lei, Chunxiao Liu, Zheng Ma, Wei Zhang, Liang Lin
|
https://iclr.cc/virtual/2019/poster/723
|
Reinforcement learning;exploration
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adaptive regularization;non-convex optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
The Case for Full-Matrix Adaptive Regularization
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
cross-lingual embeddings;evaluation;graph-based metric;modularity
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Diagnosing Language Inconsistency in Cross-Lingual Word Embeddings
| null | null | 0 | 4.333333 |
Withdraw
|
5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Model-X Knockoff Generator;model-free FDR control;variable selection
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Auto-Encoding Knockoff Generator for FDR Controlled Variable Selection
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
An Active Learning Framework for Efficient Robust Policy Search
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Transfer Learning;Reinforcement Learning;Generative Adversarial Networks;Video Games
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6 |
4;7;7
| null | null |
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Imitation Learning;Noisy Demonstration Set;Meta-Learning
| null | 0 | null | null |
iclr
| 0 | 0 |
https://sites.google.com/view/deepdj
|
main
| 4 |
4;4;4
| null | null |
Learning from Noisy Demonstration Sets via Meta-Learned Suitability Assessor
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
meta-learning;few-shot learning;incremental learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Incremental Few-Shot Learning with Attention Attractor Networks
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null |
Toyota Research Institute; Intel Labs
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Kuan-Hui Lee, German Ros, Jie Li, Adrien Gaidon
|
https://iclr.cc/virtual/2019/poster/779
|
domain adaptation;GAN;semantic segmentation;simulation;privileged information
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
SPIGAN: Privileged Adversarial Learning from Simulation
| null | null | 0 | 4.666667 |
Poster
|
5;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Disentangled representations;Variational Autoencoders;Adversarial Learning;Weakly-supervised learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Learning Disentangled Representations with Reference-Based Variational Autoencoders
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;AlphaGo Zero
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Understanding & Generalizing AlphaGo Zero
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative learning;generative models;generative query networks;camera re-localization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Learning models for visual 3D localization with implicit mapping
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Caltech; Salesforce; STATS
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey
|
https://iclr.cc/virtual/2019/poster/985
|
deep learning;generative models;imitation learning;hierarchical methods;data programming;weak supervision;spatiotemporal
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Generating Multi-Agent Trajectories using Programmatic Weak Supervision
|
https://github.com/ezhan94/multiagent-programmatic-supervision
| null | 0 | 3 |
Poster
|
3;3;3
| null |
null |
Microsoft Research, Georgia Institute of Technology; Microsoft Research, Stony Brook University; Chesapeake Conservancy; Microsoft Research, Yale University; Stony Brook University; Microsoft Research
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Nikolay Malkin, Caleb Robinson, Le Hou, Rachel Soobitsky, Jacob Czawlytko, Dimitris Samaras, Joel Saltz, Lucas Joppa, Nebojsa Jojic
|
https://iclr.cc/virtual/2019/poster/673
|
weakly supervised segmentation;land cover mapping;medical imaging
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
6;7;9
| null | null |
Label super-resolution networks
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI; Department of Electrical and Computer Engineering, University of Toronto
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Nuwan Ferdinand, Haider Al-Lawati, Stark Draper, Matthew Nokleby
|
https://iclr.cc/virtual/2019/poster/970
|
distributed optimization;gradient descent;minibatch;stragglers
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
4;7;7
| null | null |
ANYTIME MINIBATCH: EXPLOITING STRAGGLERS IN ONLINE DISTRIBUTED OPTIMIZATION
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Internal Representations;Sensitivity Analysis;Object Detection
| null | 0 | null | null |
iclr
| 0.654654 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
FAST OBJECT LOCALIZATION VIA SENSITIVITY ANALYSIS
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
few-shot learning;relation learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Few-Shot Learning by Exploiting Object Relation
| null | null | 0 | 3.666667 |
Withdraw
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
few-shot;one-shot;semi-supervised;meta-learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Projective Subspace Networks For Few-Shot Learning
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
DeepMind & Google
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yutian Chen, Yannis M Assael, Brendan Shillingford, David Budden, Scott Reed, Heiga Zen, Quan Wang, Luis C. Cobo, Andrew Trask, Ben Laurie, Caglar Gulcehre, Aaron van den Oord, Oriol Vinyals, Nando de Freitas
|
https://iclr.cc/virtual/2019/poster/786
|
few shot;meta learning;text to speech;wavenet
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Sample Efficient Adaptive Text-to-Speech
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
Department of Computer Science, University College London
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
James Townsend, Thomas Bird, David Barber
|
https://iclr.cc/virtual/2019/poster/689
|
compression;variational auto-encoders;deep latent gaussian models;lossless compression;latent variables;approximate inference;variational inference
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Practical lossless compression with latent variables using bits back coding
|
https://github.com/bits-back/bits-back
| null | 0 | 4 |
Poster
|
3;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hyperparameter search;architecture search;convolutional neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Teacher Guided Architecture Search
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Gaussian Processes;Latent Variable Model;Variational Bayes;Stan;Asset Pricing;Portfolio Allocation;Finance;CAPM
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Applications of Gaussian Processes in Finance
| null | null | 0 | 4.333333 |
Withdraw
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial robustness;feature prioritization;regularization
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Feature prioritization and regularization improve standard accuracy and adversarial robustness
| null | null | 0 | 3.333333 |
Reject
|
3;2;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
evaluation metric;predictive uncertainty;deep learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
MERCI: A NEW METRIC TO EVALUATE THE CORRELATION BETWEEN PREDICTIVE UNCERTAINTY AND TRUE ERROR
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
normalization;optimization
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
CONTROLLING COVARIATE SHIFT USING EQUILIBRIUM NORMALIZATION OF WEIGHTS
| null | null | 0 | 3 |
Reject
|
4;1;4
| null |
null |
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139; Department of Physics, University of Texas at Austin, Austin, TX, 78712; Department of Neuroscience, University of Texas at Austin, Austin, TX, 78712
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Christopher Roth, Ingmar Kanitscheider, Ila Fiete
|
https://iclr.cc/virtual/2019/poster/1061
|
RNNs;Biologically plausible learning rules;Algorithm;Neural Networks;Supervised Learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Kernel RNN Learning (KeRNL)
| null | null | 0 | 3 |
Poster
|
1;4;4
| null |
null |
Sanofi
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Jesse Johnson
|
https://iclr.cc/virtual/2019/poster/905
|
neural network;universality;expressability
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Deep, Skinny Neural Networks are not Universal Approximators
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;continuous action space;prioritization;parameter;noise;policy gradients
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Learning agents with prioritization and parameter noise in continuous state and action space
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Signal Processing Laboratory 2, EPFL, Station 11, 1015 Lausanne, Switzerland; Swiss Data Science Center, ETH Zürich, Universitätstrasse 25, 8006 Zürich, Switzerland
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Vassilis Kalofolias, Nathanaël Perraudin
|
https://iclr.cc/virtual/2019/poster/661
|
Graph learning;Graph signal processing;Network inference
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Large Scale Graph Learning From Smooth Signals
| null | null | 0 | 4 |
Poster
|
3;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
synaptic neural network;surprisal;synapse;probability;excitation;inhibition;synapse learning;bose-einstein distribution;tensor;gradient;loss function;mnist;topologically conjugate
| null | 0 | null | null |
iclr
| -0.333333 | 0 | null |
main
| 2.25 |
2;2;2;3
| null | null |
A Synaptic Neural Network and Synapse Learning
| null | null | 0 | 3.25 |
Reject
|
3;3;4;3
| null |
null |
Stanford University; MIT
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka
|
https://iclr.cc/virtual/2019/poster/791
|
graph neural networks;theory;deep learning;representational power;graph isomorphism;deep multisets
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
How Powerful are Graph Neural Networks?
| null | null | 0 | 5 |
Oral
|
5;5;5
| null |
null |
ICST, Peking University, Beijing, China; Department of Computer Science, University of Illinois at Chicago; Department of Information Science, School of Mathematical Sciences, Peking University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Wenpeng Hu, Zhou Lin, Bing Liu, Chongyang Tao, Jay Tao, Jinwen Ma, Dongyan Zhao, Rui Yan
|
https://iclr.cc/virtual/2019/poster/914
|
overcoming forgetting;model adaptation;continual learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Overcoming Catastrophic Forgetting for Continual Learning via Model Adaptation
| null | null | 0 | 3.333333 |
Poster
|
4;2;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Pipeline Optimization;Reinforcement Learning;Stochastic Computation Graph;Faster R-CNN
| null | 0 | null | null |
iclr
| -0.654654 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Reinforced Pipeline Optimization: Behaving Optimally with Non-Differentiabilities
| null | null | 0 | 3.666667 |
Reject
|
4;5;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GANs;mixed Nash equilibrium;mirror descent;sampling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Finding Mixed Nash Equilibria of Generative Adversarial Networks
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong; TuSimple; Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
LU HOU, Ruiliang Zhang, James Kwok
|
https://iclr.cc/virtual/2019/poster/969
|
weight quantization;gradient quantization;distributed learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Analysis of Quantized Models
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
University of Oxford
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Guillermo Valle-Perez, Chico Q. Camargo, Ard Louis
|
https://iclr.cc/virtual/2019/poster/989
|
generalization;deep learning theory;PAC-Bayes;Gaussian processes;parameter-function map;simplicity bias
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Deep learning generalizes because the parameter-function map is biased towards simple functions
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;goal-oriented;convolutions;off-policy
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Q-map: a Convolutional Approach for Goal-Oriented Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null |
New York University; New York University, Facebook AI Research†
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Amanpreet Singh, Tushar Jain, Sainbayar Sukhbaatar
|
https://iclr.cc/virtual/2019/poster/770
|
multiagent;communication;competitive;cooperative;continuous;emergent;reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;dqn;adversarial examples;robustness analysis;adversarial defense;robust learning;robust rl
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Distilled Agent DQN for Provable Adversarial Robustness
| null | null | 0 | 2.666667 |
Reject
|
2;2;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;recurrent neural networks;syntax;part-of-speech tagging
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
UC Berkeley and ML@B; Google Brain; UC Berkeley
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Richard Shin, Neel Kant, Kavi Gupta, Christopher Bender, Brandon Trabucco, Rishabh Singh, Dawn Song
|
https://iclr.cc/virtual/2019/poster/832
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Synthetic Datasets for Neural Program Synthesis
| null | null | 0 | 3 |
Poster
|
2;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
low precision;stochastic gradient descent
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Dimension-Free Bounds for Low-Precision Training
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
Microsoft Research Asia; KAIST; Carnegie Mellon University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Sunghoon Im, Hae-Gon Jeon, Stephen Lin, In Kweon
|
https://iclr.cc/virtual/2019/poster/682
|
Deep Learning;Stereo;Depth;Geometry
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
DPSNet: End-to-end Deep Plane Sweep Stereo
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Graph convolution;hypergraph;hyperlink prediction
| null | 0 | null | null |
iclr
| -0.981981 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Link Prediction in Hypergraphs using Graph Convolutional Networks
| null | null | 0 | 3.666667 |
Reject
|
5;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial perturbations;universal adversarial perturbations;game theory;robust machine learning
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Playing the Game of Universal Adversarial Perturbations
| null | null | 0 | 2.666667 |
Reject
|
3;4;1
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;Incremental training;Information projection;Mixture distribution
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Incremental training of multi-generative adversarial networks
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Autoencoder;dimensionality reduction;wireless positioning;channel charting;localization
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Representation-Constrained Autoencoders and an Application to Wireless Positioning
| null | null | 0 | 3.333333 |
Reject
|
4;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial attack;black-box;evolutional strategy;policy gradient
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
4;4;7
| null | null |
NATTACK: A STRONG AND UNIVERSAL GAUSSIAN BLACK-BOX ADVERSARIAL ATTACK
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null |
Department of Electronics and Information Systems (ELIS), IDLab, Ghent University, Ghent, Belgium
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Bo Kang, Jefrey Lijffijt, Tijl De Bie
|
https://iclr.cc/virtual/2019/poster/812
|
Network embedding;graph embedding;learning node representations;link prediction;multi-label classification of nodes
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Conditional Network Embeddings
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
MIT-IBM Watson AI Lab; MIT
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Ji Lin, Chuang Gan, Song Han
|
https://iclr.cc/virtual/2019/poster/863
|
defensive quantization;model quantization;adversarial attack;efficiency;robustness
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Defensive Quantization: When Efficiency Meets Robustness
| null | null | 0 | 3 |
Poster
|
3;2;4
| null |
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