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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 / 1,075 papers shown
Title
Pre-Trained Models: Past, Present and Future
Pre-Trained Models: Past, Present and Future
Xu Han
Zhengyan Zhang
Ning Ding
Yuxian Gu
Xiao Liu
...
Jie Tang
Ji-Rong Wen
Jinhui Yuan
Wayne Xin Zhao
Jun Zhu
AIFin
MQ
AI4MH
58
818
0
14 Jun 2021
What can linearized neural networks actually say about generalization?
What can linearized neural networks actually say about generalization?
Guillermo Ortiz-Jiménez
Seyed-Mohsen Moosavi-Dezfooli
P. Frossard
31
44
0
12 Jun 2021
Disrupting Model Training with Adversarial Shortcuts
Disrupting Model Training with Adversarial Shortcuts
Ivan Evtimov
Ian Covert
Aditya Kusupati
Tadayoshi Kohno
AAML
28
10
0
12 Jun 2021
What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot
  Learning for Structured Data
What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data
Yang Hu
Adriane P. Chapman
Guihua Wen
Dame Wendy Hall
46
24
0
11 Jun 2021
A Dataset And Benchmark Of Underwater Object Detection For Robot Picking
A Dataset And Benchmark Of Underwater Object Detection For Robot Picking
Chongwei Liu
Haojie Li
Shuchang Wang
Ming Zhu
Dong Wang
Xin-Yue Fan
Zhihui Wang
30
92
0
10 Jun 2021
Supervising the Transfer of Reasoning Patterns in VQA
Supervising the Transfer of Reasoning Patterns in VQA
Corentin Kervadec
Christian Wolf
G. Antipov
M. Baccouche
Madiha Nadri Wolf
35
10
0
10 Jun 2021
The dilemma of quantum neural networks
The dilemma of quantum neural networks
Yan Qian
Xinbiao Wang
Yuxuan Du
Xingyao Wu
Dacheng Tao
21
30
0
09 Jun 2021
Fractal Structure and Generalization Properties of Stochastic
  Optimization Algorithms
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms
A. Camuto
George Deligiannidis
Murat A. Erdogdu
Mert Gurbuzbalaban
Umut cSimcsekli
Lingjiong Zhu
38
29
0
09 Jun 2021
Ghosts in Neural Networks: Existence, Structure and Role of
  Infinite-Dimensional Null Space
Ghosts in Neural Networks: Existence, Structure and Role of Infinite-Dimensional Null Space
Sho Sonoda
Isao Ishikawa
Masahiro Ikeda
BDL
24
9
0
09 Jun 2021
NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely
  and Noisily Labeled Graphs
NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs
Enyan Dai
Charu C. Aggarwal
Suhang Wang
NoLa
27
114
0
08 Jun 2021
Encoding-dependent generalization bounds for parametrized quantum
  circuits
Encoding-dependent generalization bounds for parametrized quantum circuits
Matthias C. Caro
Elies Gil-Fuster
Johannes Jakob Meyer
Jens Eisert
R. Sweke
UQCV
21
100
0
07 Jun 2021
Antipodes of Label Differential Privacy: PATE and ALIBI
Antipodes of Label Differential Privacy: PATE and ALIBI
Mani Malek
Ilya Mironov
Karthik Prasad
I. Shilov
Florian Tramèr
16
62
0
07 Jun 2021
On Memorization in Probabilistic Deep Generative Models
On Memorization in Probabilistic Deep Generative Models
G. V. D. Burg
Christopher K. I. Williams
TDI
25
59
0
06 Jun 2021
Towards an Understanding of Benign Overfitting in Neural Networks
Towards an Understanding of Benign Overfitting in Neural Networks
Zhu Li
Zhi-Hua Zhou
Arthur Gretton
MLT
38
35
0
06 Jun 2021
AngularGrad: A New Optimization Technique for Angular Convergence of
  Convolutional Neural Networks
AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks
S. K. Roy
Mercedes Eugenia Paoletti
J. Haut
S. Dubey
Purushottam Kar
A. Plaza
B. B. Chaudhuri
ODL
32
18
0
21 May 2021
When Deep Classifiers Agree: Analyzing Correlations between Learning
  Order and Image Statistics
When Deep Classifiers Agree: Analyzing Correlations between Learning Order and Image Statistics
Iuliia Pliushch
Martin Mundt
Nicolas Lupp
Visvanathan Ramesh
13
12
0
19 May 2021
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers
  Solutions with Superior OOD Generalization
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization
Damien Teney
Ehsan Abbasnejad
Simon Lucey
Anton Van Den Hengel
53
87
0
12 May 2021
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy
  Labels
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels
Erik Englesson
Hossein Azizpour
NoLa
34
104
0
10 May 2021
Self-paced Resistance Learning against Overfitting on Noisy Labels
Self-paced Resistance Learning against Overfitting on Noisy Labels
Xiaoshuang Shi
Zhenhua Guo
Fuyong Xing
Yun Liang
Xiaofeng Zhu
NoLa
21
20
0
07 May 2021
Membership Inference Attacks on Deep Regression Models for Neuroimaging
Membership Inference Attacks on Deep Regression Models for Neuroimaging
Umang Gupta
Dmitris Stripelis
Pradeep Lam
Paul M. Thompson
J. Ambite
Greg Ver Steeg
MIACV
FedML
29
32
0
06 May 2021
A Geometric Analysis of Neural Collapse with Unconstrained Features
A Geometric Analysis of Neural Collapse with Unconstrained Features
Zhihui Zhu
Tianyu Ding
Jinxin Zhou
Xiao Li
Chong You
Jeremias Sulam
Qing Qu
40
197
0
06 May 2021
Schematic Memory Persistence and Transience for Efficient and Robust
  Continual Learning
Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning
Yuyang Gao
Giorgio Ascoli
Liang Zhao
32
4
0
05 May 2021
AdaBoost and robust one-bit compressed sensing
AdaBoost and robust one-bit compressed sensing
Geoffrey Chinot
Felix Kuchelmeister
Matthias Löffler
Sara van de Geer
35
5
0
05 May 2021
Poisoning the Unlabeled Dataset of Semi-Supervised Learning
Poisoning the Unlabeled Dataset of Semi-Supervised Learning
Nicholas Carlini
AAML
166
68
0
04 May 2021
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing
  Attack
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack
Yixu Wang
Jie Li
Hong Liu
Yan Wang
Yongjian Wu
Feiyue Huang
Rongrong Ji
AAML
25
34
0
03 May 2021
Who's Afraid of Adversarial Transferability?
Who's Afraid of Adversarial Transferability?
Ziv Katzir
Yuval Elovici
SILM
AAML
27
9
0
02 May 2021
Estimating the electrical power output of industrial devices with
  end-to-end time-series classification in the presence of label noise
Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise
Andrea Castellani
Sebastian Schmitt
Barbara Hammer
NoLa
45
18
0
01 May 2021
RATT: Leveraging Unlabeled Data to Guarantee Generalization
RATT: Leveraging Unlabeled Data to Guarantee Generalization
Saurabh Garg
Sivaraman Balakrishnan
J. Zico Kolter
Zachary Chase Lipton
34
30
0
01 May 2021
InfoNEAT: Information Theory-based NeuroEvolution of Augmenting
  Topologies for Side-channel Analysis
InfoNEAT: Information Theory-based NeuroEvolution of Augmenting Topologies for Side-channel Analysis
R. Acharya
F. Ganji
Domenic Forte
AAML
48
24
0
30 Apr 2021
MeerCRAB: MeerLICHT Classification of Real and Bogus Transients using
  Deep Learning
MeerCRAB: MeerLICHT Classification of Real and Bogus Transients using Deep Learning
Zafiirah Hosenie
S. Bloemen
P. Groot
R. Lyon
B. Scheers
...
Vanessa McBride
R. L. le Poole
K. Paterson
D. Pieterse
P. Woudt
34
7
0
28 Apr 2021
If your data distribution shifts, use self-learning
If your data distribution shifts, use self-learning
E. Rusak
Steffen Schneider
George Pachitariu
L. Eck
Peter V. Gehler
Oliver Bringmann
Wieland Brendel
Matthias Bethge
VLM
OOD
TTA
81
30
0
27 Apr 2021
Demystification of Few-shot and One-shot Learning
Demystification of Few-shot and One-shot Learning
I. Tyukin
A. Gorban
Muhammad H. Alkhudaydi
Qinghua Zhou
29
13
0
25 Apr 2021
Intentional Deep Overfit Learning (IDOL): A Novel Deep Learning Strategy
  for Adaptive Radiation Therapy
Intentional Deep Overfit Learning (IDOL): A Novel Deep Learning Strategy for Adaptive Radiation Therapy
J. Chun
Justin C. Park
S. Olberg
You Zhang
D. Nguyen
Jing Wang
Jin Sung Kim
Steve B. Jiang
38
22
0
23 Apr 2021
Learning from Noisy Labels for Entity-Centric Information Extraction
Learning from Noisy Labels for Entity-Centric Information Extraction
Wenxuan Zhou
Muhao Chen
NoLa
20
65
0
17 Apr 2021
Memorisation versus Generalisation in Pre-trained Language Models
Memorisation versus Generalisation in Pre-trained Language Models
Michael Tänzer
Sebastian Ruder
Marek Rei
94
50
0
16 Apr 2021
Generalization bounds via distillation
Generalization bounds via distillation
Daniel J. Hsu
Ziwei Ji
Matus Telgarsky
Lan Wang
FedML
35
32
0
12 Apr 2021
Procrustean Training for Imbalanced Deep Learning
Procrustean Training for Imbalanced Deep Learning
Han-Jia Ye
De-Chuan Zhan
Wei-Lun Chao
29
31
0
05 Apr 2021
The surprising impact of mask-head architecture on novel class
  segmentation
The surprising impact of mask-head architecture on novel class segmentation
Vighnesh Birodkar
Zhichao Lu
Siyang Li
V. Rathod
Jonathan Huang
ISeg
38
27
0
01 Apr 2021
Learning from Noisy Labels via Dynamic Loss Thresholding
Learning from Noisy Labels via Dynamic Loss Thresholding
Hao Yang
Youzhi Jin
Zi-Hua Li
Deng-Bao Wang
Lei Miao
Xin Geng
Min-Ling Zhang
NoLa
AI4CE
32
6
0
01 Apr 2021
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to
  Improve Generalization
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization
Zeke Xie
Li-xin Yuan
Zhanxing Zhu
Masashi Sugiyama
32
29
0
31 Mar 2021
Collaborative Label Correction via Entropy Thresholding
Collaborative Label Correction via Entropy Thresholding
Hao Wu
Jiangchao Yao
Jiajie Wang
Yinru Chen
Ya Zhang
Yanfeng Wang
NoLa
22
4
0
31 Mar 2021
Progressive Domain Expansion Network for Single Domain Generalization
Progressive Domain Expansion Network for Single Domain Generalization
Lei Li
Ke Gao
Juan Cao
Ziyao Huang
Yepeng Weng
Xiaoyue Mi
Zhengze Yu
Xiaoya Li
Boyang Xia
OOD
AI4CE
27
159
0
30 Mar 2021
Robust Audio-Visual Instance Discrimination
Robust Audio-Visual Instance Discrimination
Pedro Morgado
Ishan Misra
Nuno Vasconcelos
SSL
22
110
0
29 Mar 2021
AlignMixup: Improving Representations By Interpolating Aligned Features
AlignMixup: Improving Representations By Interpolating Aligned Features
Shashanka Venkataramanan
Ewa Kijak
Laurent Amsaleg
Yannis Avrithis
WSOL
35
61
0
29 Mar 2021
Understanding the role of importance weighting for deep learning
Understanding the role of importance weighting for deep learning
Da Xu
Yuting Ye
Chuanwei Ruan
FAtt
39
43
0
28 Mar 2021
From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose
  Estimation
From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation
Chen Li
G. Lee
OOD
19
81
0
27 Mar 2021
Jo-SRC: A Contrastive Approach for Combating Noisy Labels
Jo-SRC: A Contrastive Approach for Combating Noisy Labels
Yazhou Yao
Zeren Sun
Chuanyi Zhang
Fumin Shen
Qi Wu
Jian Zhang
Zhenmin Tang
NoLa
33
133
0
24 Mar 2021
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely
  Self-supervised Neural Architecture Search
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search
Changlin Li
Tao Tang
Guangrun Wang
Jiefeng Peng
Bing Wang
Xiaodan Liang
Xiaojun Chang
ViT
50
105
0
23 Mar 2021
The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison
  Linear Classifiers?
The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?
Antonio Emanuele Cinà
Sebastiano Vascon
Ambra Demontis
Battista Biggio
Fabio Roli
Marcello Pelillo
AAML
32
9
0
23 Mar 2021
Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic
  Segmentation
Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation
Yaoru Luo
Guole Liu
Yuanhao Guo
Ge Yang
NoLa
30
9
0
22 Mar 2021
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