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Understanding deep learning requires rethinking generalization

Understanding deep learning requires rethinking generalization

10 November 2016
Chiyuan Zhang
Samy Bengio
Moritz Hardt
Benjamin Recht
Oriol Vinyals
    HAI
ArXivPDFHTML

Papers citing "Understanding deep learning requires rethinking generalization"

50 / 927 papers shown
Title
Explainable Deep Learning: A Field Guide for the Uninitiated
Explainable Deep Learning: A Field Guide for the Uninitiated
Gabrielle Ras
Ning Xie
Marcel van Gerven
Derek Doran
AAML
XAI
41
371
0
30 Apr 2020
A Perspective on Deep Learning for Molecular Modeling and Simulations
A Perspective on Deep Learning for Molecular Modeling and Simulations
Jun Zhang
Yao-Kun Lei
Zhen Zhang
Junhan Chang
Maodong Li
Xu Han
Lijiang Yang
Yuqing Yang
Y. Gao
AI4CE
37
8
0
25 Apr 2020
Random Features for Kernel Approximation: A Survey on Algorithms,
  Theory, and Beyond
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond
Fanghui Liu
Xiaolin Huang
Yudong Chen
Johan A. K. Suykens
BDL
44
172
0
23 Apr 2020
On the Compressive Power of Boolean Threshold Autoencoders
On the Compressive Power of Boolean Threshold Autoencoders
A. Melkman
Sini Guo
W. Ching
Pengyu Liu
Tatsuya Akutsu
AI4CE
16
3
0
21 Apr 2020
How to Teach DNNs to Pay Attention to the Visual Modality in Speech
  Recognition
How to Teach DNNs to Pay Attention to the Visual Modality in Speech Recognition
George Sterpu
Christian Saam
N. Harte
34
28
0
17 Apr 2020
On the interplay between physical and content priors in deep learning
  for computational imaging
On the interplay between physical and content priors in deep learning for computational imaging
Mo Deng
Shuai Li
Iksung Kang
N. Fang
George Barbastathis
39
26
0
14 Apr 2020
Gradient Centralization: A New Optimization Technique for Deep Neural
  Networks
Gradient Centralization: A New Optimization Technique for Deep Neural Networks
Hongwei Yong
Jianqiang Huang
Xiansheng Hua
Lei Zhang
ODL
27
183
0
03 Apr 2020
Self-Augmentation: Generalizing Deep Networks to Unseen Classes for
  Few-Shot Learning
Self-Augmentation: Generalizing Deep Networks to Unseen Classes for Few-Shot Learning
Jinhwan Seo
Hong G Jung
Seong-Whan Lee
SSL
12
39
0
01 Apr 2020
Information Leakage in Embedding Models
Information Leakage in Embedding Models
Congzheng Song
A. Raghunathan
MIACV
21
262
0
31 Mar 2020
Regularizing Class-wise Predictions via Self-knowledge Distillation
Regularizing Class-wise Predictions via Self-knowledge Distillation
Sukmin Yun
Jongjin Park
Kimin Lee
Jinwoo Shin
29
274
0
31 Mar 2020
Dataless Model Selection with the Deep Frame Potential
Dataless Model Selection with the Deep Frame Potential
Calvin Murdock
Simon Lucey
38
6
0
30 Mar 2020
Unpacking Information Bottlenecks: Unifying Information-Theoretic
  Objectives in Deep Learning
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning
Andreas Kirsch
Clare Lyle
Y. Gal
27
16
0
27 Mar 2020
What Deep CNNs Benefit from Global Covariance Pooling: An Optimization
  Perspective
What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective
Qilong Wang
Li Zhang
Banggu Wu
Dongwei Ren
P. Li
W. Zuo
Q. Hu
19
21
0
25 Mar 2020
Learn to Forget: Machine Unlearning via Neuron Masking
Learn to Forget: Machine Unlearning via Neuron Masking
Yang Liu
Zhuo Ma
Ximeng Liu
Jian-wei Liu
Zhongyuan Jiang
Jianfeng Ma
Philip Yu
K. Ren
MU
22
61
0
24 Mar 2020
Dynamic Hierarchical Mimicking Towards Consistent Optimization Objectives
Duo Li
Qifeng Chen
153
19
0
24 Mar 2020
Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep
  Network Losses
Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses
Charles G. Frye
James B. Simon
Neha S. Wadia
A. Ligeralde
M. DeWeese
K. Bouchard
ODL
16
2
0
23 Mar 2020
On Calibration of Mixup Training for Deep Neural Networks
On Calibration of Mixup Training for Deep Neural Networks
Juan Maroñas
D. Ramos-Castro
Roberto Paredes Palacios
UQCV
30
6
0
22 Mar 2020
A comprehensive study on the prediction reliability of graph neural
  networks for virtual screening
A comprehensive study on the prediction reliability of graph neural networks for virtual screening
Soojung Yang
K. Lee
Seongok Ryu
19
7
0
17 Mar 2020
What Information Does a ResNet Compress?
What Information Does a ResNet Compress?
L. N. Darlow
Amos Storkey
SSL
30
11
0
13 Mar 2020
Analyzing Visual Representations in Embodied Navigation Tasks
Analyzing Visual Representations in Embodied Navigation Tasks
Erik Wijmans
Julian Straub
Dhruv Batra
Irfan Essa
Judy Hoffman
Ari S. Morcos
17
2
0
12 Mar 2020
SASL: Saliency-Adaptive Sparsity Learning for Neural Network
  Acceleration
SASL: Saliency-Adaptive Sparsity Learning for Neural Network Acceleration
Jun Shi
Jianfeng Xu
K. Tasaka
Zhibo Chen
6
25
0
12 Mar 2020
A Mean-field Analysis of Deep ResNet and Beyond: Towards Provable
  Optimization Via Overparameterization From Depth
A Mean-field Analysis of Deep ResNet and Beyond: Towards Provable Optimization Via Overparameterization From Depth
Yiping Lu
Chao Ma
Yulong Lu
Jianfeng Lu
Lexing Ying
MLT
39
78
0
11 Mar 2020
SuperMix: Supervising the Mixing Data Augmentation
SuperMix: Supervising the Mixing Data Augmentation
Ali Dabouei
Sobhan Soleymani
Fariborz Taherkhani
Nasser M. Nasrabadi
19
98
0
10 Mar 2020
AL2: Progressive Activation Loss for Learning General Representations in
  Classification Neural Networks
AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks
Majed El Helou
Frederike Dumbgen
Sabine Süsstrunk
CLL
AI4CE
30
2
0
07 Mar 2020
The Variational InfoMax Learning Objective
The Variational InfoMax Learning Objective
Vincenzo Crescimanna
Bruce P. Graham
16
0
0
07 Mar 2020
Combating noisy labels by agreement: A joint training method with
  co-regularization
Combating noisy labels by agreement: A joint training method with co-regularization
Hongxin Wei
Lei Feng
Xiangyu Chen
Bo An
NoLa
319
498
0
05 Mar 2020
Analyzing Accuracy Loss in Randomized Smoothing Defenses
Analyzing Accuracy Loss in Randomized Smoothing Defenses
Yue Gao
Harrison Rosenberg
Kassem Fawaz
S. Jha
Justin Hsu
AAML
24
6
0
03 Mar 2020
Towards Noise-resistant Object Detection with Noisy Annotations
Towards Noise-resistant Object Detection with Noisy Annotations
Junnan Li
Caiming Xiong
R. Socher
Guosheng Lin
ObjD
NoLa
62
28
0
03 Mar 2020
Iterative Averaging in the Quest for Best Test Error
Iterative Averaging in the Quest for Best Test Error
Diego Granziol
Xingchen Wan
Samuel Albanie
Stephen J. Roberts
10
3
0
02 Mar 2020
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy
  Regime
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime
Stéphane dÁscoli
Maria Refinetti
Giulio Biroli
Florent Krzakala
93
152
0
02 Mar 2020
Out-of-Distribution Generalization via Risk Extrapolation (REx)
Out-of-Distribution Generalization via Risk Extrapolation (REx)
David M. Krueger
Ethan Caballero
J. Jacobsen
Amy Zhang
Jonathan Binas
Dinghuai Zhang
Rémi Le Priol
Aaron Courville
OOD
215
901
0
02 Mar 2020
Do CNNs Encode Data Augmentations?
Do CNNs Encode Data Augmentations?
Eddie Q. Yan
Yanping Huang
OOD
13
5
0
29 Feb 2020
Overfitting in adversarially robust deep learning
Overfitting in adversarially robust deep learning
Leslie Rice
Eric Wong
Zico Kolter
47
785
0
26 Feb 2020
Predicting Neural Network Accuracy from Weights
Predicting Neural Network Accuracy from Weights
Thomas Unterthiner
Daniel Keysers
Sylvain Gelly
Olivier Bousquet
Ilya O. Tolstikhin
30
101
0
26 Feb 2020
Understanding Self-Training for Gradual Domain Adaptation
Understanding Self-Training for Gradual Domain Adaptation
Ananya Kumar
Tengyu Ma
Percy Liang
CLL
TTA
28
227
0
26 Feb 2020
Convex Geometry and Duality of Over-parameterized Neural Networks
Convex Geometry and Duality of Over-parameterized Neural Networks
Tolga Ergen
Mert Pilanci
MLT
42
54
0
25 Feb 2020
On Feature Normalization and Data Augmentation
On Feature Normalization and Data Augmentation
Boyi Li
Felix Wu
Ser-Nam Lim
Serge J. Belongie
Kilian Q. Weinberger
21
134
0
25 Feb 2020
Understanding and Mitigating the Tradeoff Between Robustness and
  Accuracy
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy
Aditi Raghunathan
Sang Michael Xie
Fanny Yang
John C. Duchi
Percy Liang
AAML
48
222
0
25 Feb 2020
Coherent Gradients: An Approach to Understanding Generalization in
  Gradient Descent-based Optimization
Coherent Gradients: An Approach to Understanding Generalization in Gradient Descent-based Optimization
S. Chatterjee
ODL
OOD
11
48
0
25 Feb 2020
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast
  Convergence
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence
Nicolas Loizou
Sharan Vaswani
I. Laradji
Simon Lacoste-Julien
27
181
0
24 Feb 2020
The Early Phase of Neural Network Training
The Early Phase of Neural Network Training
Jonathan Frankle
D. Schwab
Ari S. Morcos
21
170
0
24 Feb 2020
An Optimization and Generalization Analysis for Max-Pooling Networks
An Optimization and Generalization Analysis for Max-Pooling Networks
Alon Brutzkus
Amir Globerson
MLT
AI4CE
16
4
0
22 Feb 2020
Generalisation error in learning with random features and the hidden
  manifold model
Generalisation error in learning with random features and the hidden manifold model
Federica Gerace
Bruno Loureiro
Florent Krzakala
M. Mézard
Lenka Zdeborová
25
165
0
21 Feb 2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
A. Wilson
Pavel Izmailov
UQCV
BDL
OOD
24
639
0
20 Feb 2020
Implicit Regularization of Random Feature Models
Implicit Regularization of Random Feature Models
Arthur Jacot
Berfin Simsek
Francesco Spadaro
Clément Hongler
Franck Gabriel
31
82
0
19 Feb 2020
Identifying Critical Neurons in ANN Architectures using Mixed Integer
  Programming
Identifying Critical Neurons in ANN Architectures using Mixed Integer Programming
M. Elaraby
Guy Wolf
Margarida Carvalho
26
5
0
17 Feb 2020
Learning Not to Learn in the Presence of Noisy Labels
Learning Not to Learn in the Presence of Noisy Labels
Liu Ziyin
Blair Chen
Ru Wang
Paul Pu Liang
Ruslan Salakhutdinov
Louis-Philippe Morency
Masahito Ueda
NoLa
26
18
0
16 Feb 2020
Stress Test Evaluation of Transformer-based Models in Natural Language
  Understanding Tasks
Stress Test Evaluation of Transformer-based Models in Natural Language Understanding Tasks
Carlos Aspillaga
Andrés Carvallo
Vladimir Araujo
ELM
44
31
0
14 Feb 2020
Self-Distillation Amplifies Regularization in Hilbert Space
Self-Distillation Amplifies Regularization in Hilbert Space
H. Mobahi
Mehrdad Farajtabar
Peter L. Bartlett
33
226
0
13 Feb 2020
The Conditional Entropy Bottleneck
The Conditional Entropy Bottleneck
Ian S. Fischer
OOD
27
115
0
13 Feb 2020
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