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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
Department of Computer Science, UC San Diego; Department of Music, UC San Diego
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Chris Donahue, Julian McAuley, Miller Puckette
|
https://iclr.cc/virtual/2019/poster/892
|
audio;waveform;spectrogram;GAN;adversarial;WaveGAN;SpecGAN
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Adversarial Audio Synthesis
| 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 networks;decision trees;computer vision
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Adaptive Neural Trees
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Machine Translation;Multi-lingual processing;Zero-Shot translation
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
The Missing Ingredient in Zero-Shot Neural Machine Translation
| null | null | 0 | 3.666667 |
Withdraw
|
3;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Optimal transport;Wasserstein gradient;Generative adversarial network;Unsupervised learning
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Wasserstein proximal of GANs
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null |
GMEMS Technologies and Spectimbre, 366 Fairview Way, Milpitas, CA 95035
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
XI-LIN LI
|
https://iclr.cc/virtual/2019/poster/762
|
preconditioner;stochastic gradient descent;Newton method;Fisher information;natural gradient;Lie group
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Preconditioner on Matrix Lie Group for SGD
| null | null | 0 | 4.333333 |
Poster
|
5;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;experience replay;policy gradients
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Remember and Forget for Experience Replay
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Multi Agent;policy gradient
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
COLLABORATIVE MULTIAGENT REINFORCEMENT LEARNING IN HOMOGENEOUS SWARMS
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
image;translation;unsupervised;model-based
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Unsupervised Image to Sequence Translation with Canvas-Drawer Networks
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
domain adaptation;variational inference;multi-domain
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Variational Domain Adaptation
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null |
Department of Computer Science, University of California, Los Angeles; Google Research
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Patrick CHen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh
|
https://iclr.cc/virtual/2019/poster/1014
|
fast inference;softmax computation;natural language processing
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;GANs;Augmentation;Generative Modelling
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
PA-GAN: Improving GAN Training by Progressive Augmentation
| null | null | 0 | 3.666667 |
Reject
|
2;4;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;shape bias;vision;feature selection
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5 |
4;4;7
| null | null |
What a difference a pixel makes: An empirical examination of features used by CNNs for categorisation
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Cogent Labs; DeepMind
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Tiago Ramalho, Marta Garnelo
|
https://iclr.cc/virtual/2019/poster/988
|
metalearning;memory;few-shot;relational;self-attention;classification;sequential;reasoning;working memory;episodic memory
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Gradient Descent;Hessian;Deep Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Gradient Descent Happens in a Tiny Subspace
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adaptive kernels;Dynamic kernels;Pattern recognition;low memory CNNs
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Adaptive Convolutional Neural Networks
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
University of Cambridge, UK; Department of Electrical and Computer Engineering, UCLA, California, USA; Alan Turing Institute, London, UK; Department of Electrical and Computer Engineering, UCLA, California, USA; Engineering Science Department, University of Oxford, UK
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
James Jordon, Jinsung Yoon, Mihaela Schaar
|
https://iclr.cc/virtual/2019/poster/1012
|
Knockoff model;Feature selection;False discovery rate control;Generative Adversarial networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.666667 |
6;7;10
| null | null |
KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks
| null | null | 0 | 4 |
Oral
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised;machine translation;dual learning;zero-shot
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Zero-shot Dual Machine Translation
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
Mila, Université de Montréal; Element AI; Mila, Polytechnique Montréal
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Alexandre Piche, Valentin Thomas, Cyril Ibrahim, Yoshua Bengio, Christopher Pal
|
https://iclr.cc/virtual/2019/poster/783
|
control as inference;probabilistic planning;sequential monte carlo;model based reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Probabilistic Planning with Sequential Monte Carlo methods
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
University of Washington, Roboti LLC; University of Washington; OpenAI
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Kendall Lowrey, Aravind Rajeswaran, Sham M Kakade, Emanuel Todorov, Igor Mordatch
|
https://iclr.cc/virtual/2019/poster/907
|
deep reinforcement learning;exploration;model-based
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
| null | null | 0 | 4 |
Poster
|
3;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;constraints;finite state machines
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Constraining Action Sequences with Formal Languages for Deep Reinforcement Learning
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Tencent AI Lab
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Meng Fang, Cheng Zhou, Bei Shi, Boqing Gong, Jia Xu, Tong Zhang
|
https://iclr.cc/virtual/2019/poster/775
|
Sparse rewards;Dynamic goals;Experience replay
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
DHER: Hindsight Experience Replay for Dynamic Goals
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
symbolic reasoning;neural networks;natural language processing;question answering;sentence embeddings;evolution strategies
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
NLProlog: Reasoning with Weak Unification for Natural Language Question Answering
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Cross-entropy loss;Binary classification;Low-rank features;Adversarial examples;Differential training
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5 |
3;4;5;5;8
| null | null |
Cross-Entropy Loss Leads To Poor Margins
| null | null | 0 | 4 |
Reject
|
4;5;4;4;3
| null |
null |
Allen Institute for Artificial Intelligence; University of Washington; California Institute of Technology
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Hsin-Yuan Huang, Eunsol Choi, Wen-tau Yih
|
https://iclr.cc/virtual/2019/poster/761
|
Machine Comprehension;Conversational Agent;Natural Language Processing;Deep Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
FlowQA: Grasping Flow in History for Conversational Machine Comprehension
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
Department of Computer Science, University of Freiburg; Department of Computer Science, University of Freiburg; Bosch Center for Artificial Intelligence, Robert Bosch GmbH
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Frederic Runge, Danny Stoll, Stefan Falkner, Frank Hutter
|
https://iclr.cc/virtual/2019/poster/921
|
matter engineering;bioinformatics;rna design;reinforcement learning;meta learning;neural architecture search;hyperparameter optimization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Learning to Design RNA
| null | null | 0 | 3 |
Poster
|
1;4;4
| null |
null |
Department of Computing, Imperial College London, UK
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Grigorios Chrysos, Jean Kossaifi, Stefanos Zafeiriou
|
https://iclr.cc/virtual/2019/poster/1105
|
conditional GAN;unsupervised pathway;autoencoder;robustness
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Robust Conditional Generative Adversarial Networks
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Harvard Medical School; Harvard Medical School, Broad Institute of Harvard and MIT; Harvard Medical School, Present address: Massachusetts Institute of Technology; Harvard Medical School, Dana-Farber Cancer Institute, Broad Institute of Harvard and MIT
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
John Ingraham, Adam J Riesselman, Chris Sander, Debora Marks
|
https://iclr.cc/virtual/2019/poster/959
|
generative models;simulators;molecular modeling;proteins;structured prediction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.5 |
6;6;7;7
| null | null |
Learning Protein Structure with a Differentiable Simulator
| null | null | 0 | 4 |
Oral
|
3;5;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative Model;Images of Irradiation Experiments;Prior Knowledge;Attention Mechanism
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3
| null | null |
Generative Model For Material Irradiation Experiments Based On Prior Knowledge And Attention Mechanism
| null | null | 0 | 4 |
Withdraw
|
4;4
| null |
null |
Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Karel Chvalovský
|
https://iclr.cc/virtual/2019/poster/1026
|
logic;formula;recursive neural networks;recurrent neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Top-Down Neural Model For Formulae
| null | null | 0 | 3 |
Poster
|
2;4;3
| null |
null |
Department of Computer Science, University of Virginia
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Xiao Zhang, David Evans
|
https://iclr.cc/virtual/2019/poster/1128
|
Certified robustness;Adversarial examples;Cost-sensitive learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
5;5;8
| null | null |
Cost-Sensitive Robustness against Adversarial Examples
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Ensemble Convolutional Neural Networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
2;4;4
| null | null |
IEA: Inner Ensemble Average within a convolutional neural network
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
compression;architecture search
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Architecture Compression
| 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 augmentation;influence function;generative adversarial network
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Learning to Augment Influential Data
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;VAE;likelihood estimation;statistical inference
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative models;autoencoders;optimal transport;sinkhorn algorithm
| null | 0 | null | null |
iclr
| -0.870388 | 0 | null |
main
| 6.25 |
5;6;7;7
| null | null |
Sinkhorn AutoEncoders
| null | null | 0 | 3.25 |
Reject
|
4;3;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
SGD;Bayesian;RMSprop;Adam
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
A unified theory of adaptive stochastic gradient descent as Bayesian filtering
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph embedding;set function;representation learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.25 |
4;5;5;7
| null | null |
P^2IR: Universal Deep Node Representation via Partial Permutation Invariant Set Functions
| null | null | 0 | 4 |
Reject
|
4;5;3;4
| null |
null |
School of Mathematics, Institute for Advanced Study, Princeton, NJ 08540; Toyota Technological Institute at Chicago, Chicago, IL 60637; Department of Computer Science, New York University, New York, NY 10012; Department of Computer Science, Princeton University, Princeton, NJ 08540
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro
|
https://iclr.cc/virtual/2019/poster/886
|
Generalization;Over-Parametrization;Neural Networks;Deep Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
The role of over-parametrization in generalization of neural networks
| null | null | 0 | 3.666667 |
Poster
|
3;5;3
| null |
null |
University of Tübingen & U. of Edinburgh; University of Tübingen; University of Tübingen & IMPRS-IS
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix Wichmann, Wieland Brendel
|
https://iclr.cc/virtual/2019/poster/697
|
deep learning;psychophysics;representation learning;object recognition;robustness;neural networks;data augmentation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.666667 |
7;8;8
| null | null |
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
| null | null | 0 | 4 |
Oral
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Examples
| null | 0 | null | null |
iclr
| -0.617213 | 0 | null |
main
| 6 |
3;6;7;8
| null | null |
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data
| null | null | 0 | 3.5 |
Reject
|
4;4;4;2
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
noise robust;object detection
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Dept. of Electrical and Systems Engineering, University of Pennsylvania; Courant Institute of Mathematical Sciences, New York University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Fernando Gama, Alejandro Ribeiro, Joan Bruna
|
https://iclr.cc/virtual/2019/poster/1130
|
graph neural networks;deep learning;stability;scattering transforms;convolutional neural networks
| null | 0 | null | null |
iclr
| 0.654654 | 0 | null |
main
| 7.333333 |
6;7;9
| null | null |
Diffusion Scattering Transforms on Graphs
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
molecular graphs;conditional autoencoder;graph autoencoder
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4 |
3;4;5
| null | null |
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
few-shot classification;meta-learning;individualized feature space
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Meta-Learning with Individualized Feature Space for Few-Shot Classification
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
xinyi zhang, Lihui Chen
|
https://iclr.cc/virtual/2019/poster/932
|
CapsNet;Graph embedding;GNN
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Capsule Graph Neural Network
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
unsupervised learning;computer vision;object detection
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Found by NEMO: Unsupervised Object Detection from Negative Examples and Motion
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.777778 | 0 | null |
main
| 5.5 |
4;6;6;6
| null | null |
CAML: Fast Context Adaptation via Meta-Learning
| null | null | 0 | 3.25 |
Reject
|
5;2;2;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
bayesian filtering;heteroscedastic noise;deep learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
On Learning Heteroscedastic Noise Models within Differentiable Bayes Filters
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Department of Computer Science, University of Rochester, Rochester, USA; Department of Computer Science, University of Rochester, Rochester, USA; Kwai AI Lab at Seattle, Seattle, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Haichuan Yang, Yuhao Zhu, Ji Liu
|
https://iclr.cc/virtual/2019/poster/965
|
model compression;inference energy saving;deep neural network pruning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
Facebook AI Research & The School of Computer Science, Tel Aviv University; The School of Computer Science, Tel Aviv University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Ori Press, Tomer Galanti, Sagie Benaim, Lior Wolf
|
https://iclr.cc/virtual/2019/poster/687
|
Image-to-image Translation;Disentanglement;Autoencoders;Faces
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer
| null | null | 0 | 2 |
Poster
|
2;1;3
| null |
null |
University of Science and Technology of China; Duke University; Microsoft Research, Asia; The Ohio State University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yi Zhou, Junjie Yang, Huishuai Zhang, Yingbin Liang, VAHID TAROKH
|
https://iclr.cc/virtual/2019/poster/882
|
SGD;deep learning;global minimum;convergence
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
SGD Converges to Global Minimum in Deep Learning via Star-convex Path
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
Apple Inc; Duke University; Petuum Inc
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric Xing
|
https://iclr.cc/virtual/2019/poster/961
| null | null | 0 | null | null |
iclr
| -0.802955 | 0 | null |
main
| 6.666667 |
4;7;9
| null | null |
Toward Understanding the Impact of Staleness in Distributed Machine Learning
| null | null | 0 | 4.666667 |
Poster
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial example;knowledge representation;distribution imitation
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 2.666667 |
2;3;3
| null | null |
Explaining Adversarial Examples with Knowledge Representation
| null | null | 0 | 3.666667 |
Reject
|
5;2;4
| null |
null |
Under double-blind review
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Zining Zhu, Jekaterina Novikova, Frank Rudzicz
| null | null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Isolating effects of age with fair representation learning when assessing dementia
| null | null | 0 | 3.333333 |
Withdraw
|
3;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bregman's Dilemma;Generalization Error;Margin;Spectral normalization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
ON BREIMAN’S DILEMMA IN NEURAL NETWORKS: SUCCESS AND FAILURE OF NORMALIZED MARGINS
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
NLP;LSTM;Compression;Low Rank;Norm Analysis
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Low-Rank Matrix Factorization of LSTM as Effective Model Compression
| null | null | 0 | 3.333333 |
Withdraw
|
4;2;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Intuitive Physics;Stability Prediction;Adversarial Training;Auxiliary Training;Multi-Task Learning
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Guiding Physical Intuition with Neural Stethoscopes
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
Shanghai Key Laboratory of Data Science, Fudan University; Shanghai Key Laboratory of Intelligent Information Processing, Fudan University; Shanghai University of Finance and Economics
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Wanyun Cui, Guangyu Zheng, Zhiqiang Shen, Sihang Jiang, Wei Wang
|
https://iclr.cc/virtual/2019/poster/735
|
transfer learning;recurrent neural network;attention;natural language processing
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Transfer Learning for Sequences via Learning to Collocate
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generatice models;causality;disentangled representations
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Tinkering with black boxes: counterfactuals uncover modularity in generative models
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
Department of Computer Science, Stanford University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Karan Goel, Emma Brunskill
|
https://iclr.cc/virtual/2019/poster/1057
|
learning procedural abstractions;latent variable modeling;evaluation criteria
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure
| null | null | 0 | 2.666667 |
Poster
|
2;3;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
2;3;5
| null | null |
Logit Regularization Methods for Adversarial Robustness
| null | null | 0 | 5 |
Withdraw
|
5;5;5
| null |
null |
Under double-blind review
|
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Sequence-to-sequence;adversarial attacks;evaluation;meaning preservation;machine translation
| null | 0 | null | null |
iclr
| -0.927173 | 0 | null |
main
| 4.25 |
3;4;4;6
| null | null |
On Meaning-Preserving Adversarial Perturbations for Sequence-to-Sequence Models
| null | null | 0 | 3.75 |
Reject
|
4;4;4;3
| null |
null |
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Tencent AI Lab, Bellevue, WA 98004, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Chih-Kuan Yeh, Jianshu Chen, Chengzhu Yu, Dong Yu
|
https://iclr.cc/virtual/2019/poster/641
|
Unsupervised speech recognition;unsupervised learning;phoneme classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Technical University of Munich, Germany
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Daniel Zügner, Stephan Günnemann
|
https://iclr.cc/virtual/2019/poster/826
|
graph mining;adversarial attacks;meta learning;graph neural networks;node classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Adversarial Attacks on Graph Neural Networks via Meta Learning
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Monash University, Australia; Defence Science and Technology Group, Department of Defence, Australia; AI Research Lab, Trusting Social, Australia; Data61, CSIRO, Australia
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Tue Le, Tuan Nguyen, Trung Le, Dinh Phung, Paul Montague, Olivier Vel, Lizhen Qu
|
https://iclr.cc/virtual/2019/poster/1135
|
Vulnerabilities Detection;Sequential Auto-Encoder;Separable Representation
| null | 0 | null | null |
iclr
| -0.522233 | 0 | null |
main
| 6.25 |
6;6;6;7
| null | null |
Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection
| null | null | 0 | 2.75 |
Poster
|
2;3;4;2
| null |
null |
Google Brain, USA; University of Texas at Austin, USA; Google Brain, USA; IISc Bangalore, India; Google Brain, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh
|
https://iclr.cc/virtual/2019/poster/869
|
neural program repair;neural program embeddings;pointer networks
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Neural Program Repair by Jointly Learning to Localize and Repair
| null | null | 0 | 4.666667 |
Poster
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
imitation learning;latent variable model;variational autoencoder;diverse behaviour
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Trajectory VAE for multi-modal imitation
| 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
| -0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Human Action Recognition Based on Spatial-Temporal Attention
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Imperial College London, ETH Zurich; ETH Zurich
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Nikolay Nikolov, Johannes Kirschner, Felix Berkenkamp, Andreas Krause
|
https://iclr.cc/virtual/2019/poster/983
|
reinforcement learning;exploration;information directed sampling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Information-Directed Exploration for Deep Reinforcement Learning
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Active Learning;Deep Learning;Bayesian Neural Networks;Bayesian Deep Learning;Ensembles
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Deep Ensemble Bayesian Active Learning : Adressing the Mode Collapse issue in Monte Carlo dropout via Ensembles
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;generalization;capacity constraints;information theory
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Policy Generalization In Capacity-Limited Reinforcement Learning
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Point Cloud;GAN
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Point Cloud GAN
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
University of Amsterdam, ORTEC; University of Amsterdam; University of Amsterdam, CIFAR
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Wouter Kool, Herke van Hoof, Max Welling
|
https://iclr.cc/virtual/2019/poster/1049
|
learning;routing problems;heuristics;attention;reinforce;travelling salesman problem;vehicle routing problem;orienteering problem;prize collecting travelling salesman problem
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Attention, Learn to Solve Routing Problems!
| null | null | 0 | 5 |
Poster
|
5;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Feature Selection;Dimension Reduction
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Locally Linear Unsupervised Feature Selection
| null | null | 0 | 4 |
Reject
|
5;5;2
| null |
null |
IBM Research, Yorktown Heights, NY 10598, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Haifeng Qian, Mark N Wegman
|
https://iclr.cc/virtual/2019/poster/1134
|
adversarial defense;regularization;robustness;generalization
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
L2-Nonexpansive Neural Networks
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Successor Features;Successor Representations;Transfer Learning;Representation Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Universal Successor Features for Transfer Reinforcement Learning
| null | null | 0 | 5 |
Reject
|
5;5;5
| 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 |
4;4;4
| null | null |
Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
2019
| 0 | null | null | 0 | null | null | null | null | null |
Hoang Thanh-Tung, Truyen Tran, Svetha Venkatesh
|
https://iclr.cc/virtual/2019/poster/896
|
GAN;generalization;gradient penalty;zero centered;convergence
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Improving Generalization and Stability of Generative Adversarial Networks
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
|
null |
Facebook AI Research, Menlo Park, CA, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Alexei Baevski, Michael Auli
|
https://iclr.cc/virtual/2019/poster/950
|
Neural language modeling
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.666667 |
7;8;8
| null | null |
Adaptive Input Representations for Neural Language Modeling
|
http://github.com/pytorch/fairseq
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Deep Feed-forward Neural Network;Recurrent Neural Network;Game Theory;Control Theory;Nash Equilibrium;Optimization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3 |
2;3;4
| null | null |
FROM DEEP LEARNING TO DEEP DEDUCING: AUTOMATICALLY TRACKING DOWN NASH EQUILIBRIUM THROUGH AUTONOMOUS NEURAL AGENT, A POSSIBLE MISSING STEP TOWARD GENERAL A.I.
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;large-scale classificaion;heirarchical classification;zero-shot learning
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 3.666667 |
2;4;5
| null | null |
DEEP HIERARCHICAL MODEL FOR HIERARCHICAL SELECTIVE CLASSIFICATION AND ZERO SHOT LEARNING
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Department of Biosystems Science and Engineering, ETH Zurich, Switzerland; SIB Swiss Institute of Bioinformatics, Switzerland
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Bastian Rieck, Matteo Togninalli, Christian Bock, Michael Moor, Max Horn, Thomas Gumbsch, and Karsten Borwardt
|
https://iclr.cc/virtual/2019/poster/909
|
Algebraic topology;persistent homology;network complexity;neural network
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
out-of-distribution detection;variational inference;Dirichlet distribution;deep learning;uncertainty measure
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
A Variational Dirichlet Framework for Out-of-Distribution Detection
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
Carnegie Mellon University
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Michael Kuchnik & Virginia Smith
|
https://iclr.cc/virtual/2019/poster/1118
|
data augmentation;invariance;subsampling;influence
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Efficient Augmentation via Data Subsampling
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Hierarchical Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.904534 | 0 | null |
main
| 4.75 |
4;4;5;6
| null | null |
Successor Options : An Option Discovery Algorithm for Reinforcement Learning
| null | null | 0 | 4.5 |
Reject
|
5;5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Model compression;distillation;generative adversarial network;GAN;deep neural network;random forest;ensemble;decision tree;convolutional neural network
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Model Compression with Generative Adversarial Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
State University of New York at Buffalo, Buffalo, NY; Microsoft Cloud & AI, Redmond, WA; Microsoft Research, Redmond, WA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
shuang ma, Daniel McDuff, Yale Song
|
https://iclr.cc/virtual/2019/poster/1111
|
Text-To-Speech synthesis;GANs
| null | 0 | null | null |
iclr
| 0.333333 | 0 |
https://researchdemopage.wixsite.com/tts-gan
|
main
| 6.25 |
6;6;6;7
| null | null |
Neural TTS Stylization with Adversarial and Collaborative Games
| null | null | 0 | 4.5 |
Poster
|
3;5;5;5
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
machine learning;causal inference;causal neural networks;deep learning;CATE estimation;transfer learning;meta-learning;causal transfer
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
3;5;7
| null | null |
Transfer Learning for Estimating Causal Effects Using Neural Networks
| null | null | 0 | 3.333333 |
Withdraw
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Representation learning;Hierarchical clustering;Nonparametric Bayesian modeling
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Hierarchically Clustered Representation Learning
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
supervised learning;backpropagation-free deep architecture;kernel method
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Learning Backpropagation-Free Deep Architectures with Kernels
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
video classification;efficient computation;knowledge distillation;teacher-student
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
A Teacher Student Network For Faster Video Classification
| null | null | 0 | 4.666667 |
Withdraw
|
5;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generalized zero-shot learning;domain division;bootstrapping;Kolmogorov-Smirnov
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Learning to Separate Domains in Generalized Zero-Shot and Open Set Learning: a probabilistic perspective
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Hierarchical Classification;Text Classification
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
End-to-End Hierarchical Text Classification with Label Assignment Policy
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial;object detection
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Hiding Objects from Detectors: Exploring Transferrable Adversarial Patterns
| null | null | 0 | 3.666667 |
Withdraw
|
3;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Stochastic gradient descent;anisotropic noise;regularization
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5 |
4;5;6
| null | null |
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Minima and Regularization Effects
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;continual learning;universal value functions;off-policy learning;multi-task
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Unicorn: Continual learning with a universal, off-policy agent
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, USA
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Yize Chen, Yuanyuan Shi, Baosen Zhang
|
https://iclr.cc/virtual/2019/poster/722
|
optimal control;input convex neural network;convex optimization
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Optimal Control Via Neural Networks: A Convex Approach
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
Kiel University / ZBW, Germany; Idiap Research Institute, Martigny, Switzerland; University of Essex, United Kingdom
|
2019
| 0 | null | null | 0 | null | null | null | null | null |
Florian Mai, Lukas Galke, Ansgar Scherp
|
https://iclr.cc/virtual/2019/poster/1054
|
Text representation learning;Sentence embedding;Efficient training scheme;word2vec
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial vulnerability;neural networks;gradients;FGSM;adversarial data-augmentation;gradient regularization;robust optimization
| null | 0 | null | null |
iclr
| -0.801784 | 0 | null |
main
| 6 |
4;5;6;9
| null | null |
Adversarial Vulnerability of Neural Networks Increases with Input Dimension
| null | null | 0 | 4.5 |
Reject
|
5;5;4;4
| null |
null | null |
2019
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Large Batch Training;Augmentation;Deep Learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
4;4;8
| null | null |
Augment your batch: better training with larger batches
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
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