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Fine-Grained Analysis of Optimization and Generalization for
  Overparameterized Two-Layer Neural Networks

Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks

24 January 2019
Sanjeev Arora
S. Du
Wei Hu
Zhiyuan Li
Ruosong Wang
    MLT
ArXivPDFHTML

Papers citing "Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks"

42 / 192 papers shown
Title
Optimization and Generalization Analysis of Transduction through
  Gradient Boosting and Application to Multi-scale Graph Neural Networks
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks
Kenta Oono
Taiji Suzuki
AI4CE
34
31
0
15 Jun 2020
Global Attention Improves Graph Networks Generalization
Global Attention Improves Graph Networks Generalization
Omri Puny
Heli Ben-Hamu
Y. Lipman
27
22
0
14 Jun 2020
Non-convergence of stochastic gradient descent in the training of deep
  neural networks
Non-convergence of stochastic gradient descent in the training of deep neural networks
Patrick Cheridito
Arnulf Jentzen
Florian Rossmannek
14
37
0
12 Jun 2020
Can Temporal-Difference and Q-Learning Learn Representation? A
  Mean-Field Theory
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
Yufeng Zhang
Qi Cai
Zhuoran Yang
Yongxin Chen
Zhaoran Wang
OOD
MLT
58
11
0
08 Jun 2020
Speedy Performance Estimation for Neural Architecture Search
Speedy Performance Estimation for Neural Architecture Search
Binxin Ru
Clare Lyle
Lisa Schut
M. Fil
Mark van der Wilk
Y. Gal
13
35
0
08 Jun 2020
Feature Purification: How Adversarial Training Performs Robust Deep
  Learning
Feature Purification: How Adversarial Training Performs Robust Deep Learning
Zeyuan Allen-Zhu
Yuanzhi Li
MLT
AAML
27
146
0
20 May 2020
Learning the gravitational force law and other analytic functions
Learning the gravitational force law and other analytic functions
Atish Agarwala
Abhimanyu Das
Rina Panigrahy
Qiuyi Zhang
MLT
6
0
0
15 May 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
32
172
0
23 Apr 2020
Analysis of Knowledge Transfer in Kernel Regime
Analysis of Knowledge Transfer in Kernel Regime
Arman Rahbar
Ashkan Panahi
Chiranjib Bhattacharyya
Devdatt Dubhashi
M. Chehreghani
13
3
0
30 Mar 2020
Frequency Bias in Neural Networks for Input of Non-Uniform Density
Frequency Bias in Neural Networks for Input of Non-Uniform Density
Ronen Basri
Meirav Galun
Amnon Geifman
David Jacobs
Yoni Kasten
S. Kritchman
34
182
0
10 Mar 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
11
4
0
22 Feb 2020
Learning Parities with Neural Networks
Learning Parities with Neural Networks
Amit Daniely
Eran Malach
15
76
0
18 Feb 2020
Convergence of End-to-End Training in Deep Unsupervised Contrastive
  Learning
Convergence of End-to-End Training in Deep Unsupervised Contrastive Learning
Zixin Wen
SSL
18
2
0
17 Feb 2020
Over-parameterized Adversarial Training: An Analysis Overcoming the
  Curse of Dimensionality
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality
Yi Zhang
Orestis Plevrakis
S. Du
Xingguo Li
Zhao-quan Song
Sanjeev Arora
21
51
0
16 Feb 2020
Proving the Lottery Ticket Hypothesis: Pruning is All You Need
Proving the Lottery Ticket Hypothesis: Pruning is All You Need
Eran Malach
Gilad Yehudai
Shai Shalev-Shwartz
Ohad Shamir
48
271
0
03 Feb 2020
Memory capacity of neural networks with threshold and ReLU activations
Memory capacity of neural networks with threshold and ReLU activations
Roman Vershynin
23
21
0
20 Jan 2020
Deep Network Approximation for Smooth Functions
Deep Network Approximation for Smooth Functions
Jianfeng Lu
Zuowei Shen
Haizhao Yang
Shijun Zhang
35
247
0
09 Jan 2020
Towards Understanding the Spectral Bias of Deep Learning
Towards Understanding the Spectral Bias of Deep Learning
Yuan Cao
Zhiying Fang
Yue Wu
Ding-Xuan Zhou
Quanquan Gu
21
214
0
03 Dec 2019
Denoising and Regularization via Exploiting the Structural Bias of
  Convolutional Generators
Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators
Reinhard Heckel
Mahdi Soltanolkotabi
DiffM
27
81
0
31 Oct 2019
Neural Spectrum Alignment: Empirical Study
Neural Spectrum Alignment: Empirical Study
Dmitry Kopitkov
Vadim Indelman
14
14
0
19 Oct 2019
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
Sanjeev Arora
S. Du
Zhiyuan Li
Ruslan Salakhutdinov
Ruosong Wang
Dingli Yu
AAML
9
161
0
03 Oct 2019
Beyond Linearization: On Quadratic and Higher-Order Approximation of
  Wide Neural Networks
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks
Yu Bai
J. Lee
11
116
0
03 Oct 2019
Towards Understanding the Transferability of Deep Representations
Towards Understanding the Transferability of Deep Representations
Hong Liu
Mingsheng Long
Jianmin Wang
Michael I. Jordan
16
25
0
26 Sep 2019
Asymptotics of Wide Networks from Feynman Diagrams
Asymptotics of Wide Networks from Feynman Diagrams
Ethan Dyer
Guy Gur-Ari
19
113
0
25 Sep 2019
The generalization error of random features regression: Precise
  asymptotics and double descent curve
The generalization error of random features regression: Precise asymptotics and double descent curve
Song Mei
Andrea Montanari
39
624
0
14 Aug 2019
Kernel and Rich Regimes in Overparametrized Models
Blake E. Woodworth
Suriya Gunasekar
Pedro H. P. Savarese
E. Moroshko
Itay Golan
J. Lee
Daniel Soudry
Nathan Srebro
19
353
0
13 Jun 2019
Norm-based generalisation bounds for multi-class convolutional neural
  networks
Norm-based generalisation bounds for multi-class convolutional neural networks
Antoine Ledent
Waleed Mustafa
Yunwen Lei
Marius Kloft
8
5
0
29 May 2019
Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for
  Regression Problems
Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems
Tianle Cai
Ruiqi Gao
Jikai Hou
Siyu Chen
Dong Wang
Di He
Zhihua Zhang
Liwei Wang
ODL
11
57
0
28 May 2019
What Can ResNet Learn Efficiently, Going Beyond Kernels?
What Can ResNet Learn Efficiently, Going Beyond Kernels?
Zeyuan Allen-Zhu
Yuanzhi Li
24
183
0
24 May 2019
Gradient Descent can Learn Less Over-parameterized Two-layer Neural
  Networks on Classification Problems
Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems
Atsushi Nitanda
Geoffrey Chinot
Taiji Suzuki
MLT
8
33
0
23 May 2019
A type of generalization error induced by initialization in deep neural
  networks
A type of generalization error induced by initialization in deep neural networks
Yaoyu Zhang
Zhi-Qin John Xu
Tao Luo
Zheng Ma
9
49
0
19 May 2019
Linearized two-layers neural networks in high dimension
Linearized two-layers neural networks in high dimension
Behrooz Ghorbani
Song Mei
Theodor Misiakiewicz
Andrea Montanari
MLT
11
241
0
27 Apr 2019
On Exact Computation with an Infinitely Wide Neural Net
On Exact Computation with an Infinitely Wide Neural Net
Sanjeev Arora
S. Du
Wei Hu
Zhiyuan Li
Ruslan Salakhutdinov
Ruosong Wang
22
899
0
26 Apr 2019
A Selective Overview of Deep Learning
A Selective Overview of Deep Learning
Jianqing Fan
Cong Ma
Yiqiao Zhong
BDL
VLM
25
136
0
10 Apr 2019
Analysis of the Gradient Descent Algorithm for a Deep Neural Network
  Model with Skip-connections
Analysis of the Gradient Descent Algorithm for a Deep Neural Network Model with Skip-connections
E. Weinan
Chao Ma
Qingcan Wang
Lei Wu
MLT
21
22
0
10 Apr 2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise
  for Overparameterized Neural Networks
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks
Mingchen Li
Mahdi Soltanolkotabi
Samet Oymak
NoLa
26
350
0
27 Mar 2019
Gradient Descent Finds Global Minima of Deep Neural Networks
Gradient Descent Finds Global Minima of Deep Neural Networks
S. Du
J. Lee
Haochuan Li
Liwei Wang
M. Tomizuka
ODL
15
1,120
0
09 Nov 2018
A Priori Estimates of the Population Risk for Two-layer Neural Networks
A Priori Estimates of the Population Risk for Two-layer Neural Networks
Weinan E
Chao Ma
Lei Wu
24
130
0
15 Oct 2018
Regularization Matters: Generalization and Optimization of Neural Nets
  v.s. their Induced Kernel
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
PAC-Bayesian Margin Bounds for Convolutional Neural Networks
PAC-Bayesian Margin Bounds for Convolutional Neural Networks
Konstantinos Pitas
Mike Davies
P. Vandergheynst
BDL
41
12
0
30 Dec 2017
High-dimensional dynamics of generalization error in neural networks
High-dimensional dynamics of generalization error in neural networks
Madhu S. Advani
Andrew M. Saxe
AI4CE
33
463
0
10 Oct 2017
Norm-Based Capacity Control in Neural Networks
Norm-Based Capacity Control in Neural Networks
Behnam Neyshabur
Ryota Tomioka
Nathan Srebro
116
577
0
27 Feb 2015
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