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1706.08947
Cited By
Exploring Generalization in Deep Learning
27 June 2017
Behnam Neyshabur
Srinadh Bhojanapalli
David A. McAllester
Nathan Srebro
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Papers citing
"Exploring Generalization in Deep Learning"
50 / 305 papers shown
Title
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
A. Wilson
Pavel Izmailov
UQCV
BDL
OOD
24
639
0
20 Feb 2020
Data-Driven Symbol Detection via Model-Based Machine Learning
Nariman Farsad
Nir Shlezinger
Andrea J. Goldsmith
Yonina C. Eldar
21
49
0
14 Feb 2020
Topologically Densified Distributions
Christoph Hofer
Florian Graf
Marc Niethammer
Roland Kwitt
27
15
0
12 Feb 2020
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima
Zeke Xie
Issei Sato
Masashi Sugiyama
ODL
20
17
0
10 Feb 2020
Revisiting Landscape Analysis in Deep Neural Networks: Eliminating Decreasing Paths to Infinity
Shiyu Liang
Ruoyu Sun
R. Srikant
27
19
0
31 Dec 2019
Locality and compositionality in zero-shot learning
Tristan Sylvain
Linda Petrini
R. Devon Hjelm
18
56
0
20 Dec 2019
Improving Model Robustness Using Causal Knowledge
T. Kyono
M. Schaar
OOD
22
12
0
27 Nov 2019
Information-Theoretic Local Minima Characterization and Regularization
Zhiwei Jia
Hao Su
21
19
0
19 Nov 2019
Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin
Colin Wei
Tengyu Ma
AAML
OOD
36
85
0
09 Oct 2019
How does topology influence gradient propagation and model performance of deep networks with DenseNet-type skip connections?
Kartikeya Bhardwaj
Guihong Li
R. Marculescu
30
1
0
02 Oct 2019
A Constructive Prediction of the Generalization Error Across Scales
Jonathan S. Rosenfeld
Amir Rosenfeld
Yonatan Belinkov
Nir Shavit
22
205
0
27 Sep 2019
PAC-Bayes with Backprop
Omar Rivasplata
Vikram Tankasali
Csaba Szepesvári
21
49
0
19 Aug 2019
On improving deep learning generalization with adaptive sparse connectivity
Shiwei Liu
D. Mocanu
Mykola Pechenizkiy
ODL
12
7
0
27 Jun 2019
The Barron Space and the Flow-induced Function Spaces for Neural Network Models
E. Weinan
Chao Ma
Lei Wu
30
109
0
18 Jun 2019
Learning to Forget for Meta-Learning
Sungyong Baik
Seokil Hong
Kyoung Mu Lee
CLL
KELM
14
87
0
13 Jun 2019
Does Learning Require Memorization? A Short Tale about a Long Tail
Vitaly Feldman
TDI
21
481
0
12 Jun 2019
Stochastic Neural Network with Kronecker Flow
Chin-Wei Huang
Ahmed Touati
Pascal Vincent
Gintare Karolina Dziugaite
Alexandre Lacoste
Aaron Courville
BDL
24
8
0
10 Jun 2019
Implicit Regularization in Deep Matrix Factorization
Sanjeev Arora
Nadav Cohen
Wei Hu
Yuping Luo
AI4CE
24
491
0
31 May 2019
Generalization bounds for deep convolutional neural networks
Philip M. Long
Hanie Sedghi
MLT
37
89
0
29 May 2019
Fine-grained Optimization of Deep Neural Networks
Mete Ozay
ODL
8
1
0
22 May 2019
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation
Colin Wei
Tengyu Ma
12
109
0
09 May 2019
Stability and Generalization of Graph Convolutional Neural Networks
Saurabh Verma
Zhi-Li Zhang
GNN
MLT
24
153
0
03 May 2019
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning
Alessandro Bianchi
Moreno Raimondo Vendra
P. Protopapas
Marco Brambilla
12
8
0
08 Apr 2019
Statistical Guarantees for the Robustness of Bayesian Neural Networks
L. Cardelli
M. Kwiatkowska
Luca Laurenti
Nicola Paoletti
A. Patané
Matthew Wicker
AAML
23
54
0
05 Mar 2019
An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise
Yeming Wen
Kevin Luk
Maxime Gazeau
Guodong Zhang
Harris Chan
Jimmy Ba
ODL
20
22
0
21 Feb 2019
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
Tiago Ramalho
M. Garnelo
BDL
33
77
0
07 Feb 2019
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks
Umut Simsekli
Levent Sagun
Mert Gurbuzbalaban
17
237
0
18 Jan 2019
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks
Difan Zou
Yuan Cao
Dongruo Zhou
Quanquan Gu
ODL
22
446
0
21 Nov 2018
Neural Lander: Stable Drone Landing Control using Learned Dynamics
Guanya Shi
Xichen Shi
Michael O'Connell
Rose Yu
Kamyar Azizzadenesheli
Anima Anandkumar
Yisong Yue
Soon-Jo Chung
16
270
0
19 Nov 2018
A Priori Estimates of the Population Risk for Two-layer Neural Networks
Weinan E
Chao Ma
Lei Wu
29
130
0
15 Oct 2018
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
Colin Wei
J. Lee
Qiang Liu
Tengyu Ma
18
243
0
12 Oct 2018
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning
Charles H. Martin
Michael W. Mahoney
AI4CE
32
190
0
02 Oct 2018
Fluctuation-dissipation relations for stochastic gradient descent
Sho Yaida
19
73
0
28 Sep 2018
Capacity Control of ReLU Neural Networks by Basis-path Norm
Shuxin Zheng
Qi Meng
Huishuai Zhang
Wei-neng Chen
Nenghai Yu
Tie-Yan Liu
24
23
0
19 Sep 2018
Approximation and Estimation for High-Dimensional Deep Learning Networks
Andrew R. Barron
Jason M. Klusowski
24
59
0
10 Sep 2018
Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization
G. Wang
G. Giannakis
Jie Chen
MLT
24
131
0
14 Aug 2018
Generalization Error in Deep Learning
Daniel Jakubovitz
Raja Giryes
M. Rodrigues
AI4CE
23
109
0
03 Aug 2018
A Mean-Field Optimal Control Formulation of Deep Learning
Weinan E
Jiequn Han
Qianxiao Li
OOD
6
181
0
03 Jul 2018
On the Spectral Bias of Neural Networks
Nasim Rahaman
A. Baratin
Devansh Arpit
Felix Dräxler
Min-Bin Lin
Fred Hamprecht
Yoshua Bengio
Aaron Courville
39
1,390
0
22 Jun 2018
PAC-Bayes Control: Learning Policies that Provably Generalize to Novel Environments
Anirudha Majumdar
M. Goldstein
Anoopkumar Sonar
20
18
0
11 Jun 2018
Understanding Batch Normalization
Johan Bjorck
Carla P. Gomes
B. Selman
Kilian Q. Weinberger
18
593
0
01 Jun 2018
Lipschitz regularity of deep neural networks: analysis and efficient estimation
Kevin Scaman
Aladin Virmaux
17
515
0
28 May 2018
Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach
Wenda Zhou
Victor Veitch
Morgane Austern
Ryan P. Adams
Peter Orbanz
32
209
0
16 Apr 2018
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds
Cenk Baykal
Lucas Liebenwein
Igor Gilitschenski
Dan Feldman
Daniela Rus
17
79
0
15 Apr 2018
On the importance of single directions for generalization
Ari S. Morcos
David Barrett
Neil C. Rabinowitz
M. Botvinick
13
328
0
19 Mar 2018
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs
Diviyan Kalainathan
Olivier Goudet
Isabelle M Guyon
David Lopez-Paz
Michèle Sebag
CML
24
93
0
13 Mar 2018
A Walk with SGD
Chen Xing
Devansh Arpit
Christos Tsirigotis
Yoshua Bengio
19
118
0
24 Feb 2018
Stronger generalization bounds for deep nets via a compression approach
Sanjeev Arora
Rong Ge
Behnam Neyshabur
Yi Zhang
MLT
AI4CE
21
630
0
14 Feb 2018
Fix your classifier: the marginal value of training the last weight layer
Elad Hoffer
Itay Hubara
Daniel Soudry
29
101
0
14 Jan 2018
Visualizing the Loss Landscape of Neural Nets
Hao Li
Zheng Xu
Gavin Taylor
Christoph Studer
Tom Goldstein
74
1,843
0
28 Dec 2017
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