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Gradient Descent Provably Optimizes Over-parameterized Neural Networks
v1v2 (latest)

Gradient Descent Provably Optimizes Over-parameterized Neural Networks

4 October 2018
S. Du
Xiyu Zhai
Barnabás Póczós
Aarti Singh
    MLTODL
ArXiv (abs)PDFHTML

Papers citing "Gradient Descent Provably Optimizes Over-parameterized Neural Networks"

50 / 882 papers shown
Title
From Optimization Dynamics to Generalization Bounds via Łojasiewicz
  Gradient Inequality
From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient Inequality
Fusheng Liu
Haizhao Yang
Soufiane Hayou
Qianxiao Li
AI4CE
81
2
0
22 Feb 2022
Theoretical Analysis of Deep Neural Networks in Physical Layer
  Communication
Theoretical Analysis of Deep Neural Networks in Physical Layer Communication
Jing Liu
Haitao Zhao
Dongtang Ma
Kai Mei
Jibo Wei
89
4
0
21 Feb 2022
Random Feature Amplification: Feature Learning and Generalization in
  Neural Networks
Random Feature Amplification: Feature Learning and Generalization in Neural Networks
Spencer Frei
Niladri S. Chatterji
Peter L. Bartlett
MLT
103
30
0
15 Feb 2022
Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient
  for Out-of-Distribution Generalization
Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization
Elan Rosenfeld
Pradeep Ravikumar
Andrej Risteski
OOD
88
82
0
14 Feb 2022
Benign Overfitting in Two-layer Convolutional Neural Networks
Benign Overfitting in Two-layer Convolutional Neural Networks
Yuan Cao
Zixiang Chen
M. Belkin
Quanquan Gu
MLT
93
90
0
14 Feb 2022
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
Spencer Frei
Niladri S. Chatterji
Peter L. Bartlett
MLT
110
75
0
11 Feb 2022
Deep Learning in Random Neural Fields: Numerical Experiments via Neural
  Tangent Kernel
Deep Learning in Random Neural Fields: Numerical Experiments via Neural Tangent Kernel
Kaito Watanabe
Kotaro Sakamoto
Ryo Karakida
Sho Sonoda
S. Amari
OOD
130
1
0
10 Feb 2022
Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets
Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets
Tianlong Chen
Xuxi Chen
Xiaolong Ma
Yanzhi Wang
Zhangyang Wang
82
34
0
09 Feb 2022
Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably
Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably
Tianyi Liu
Yan Li
Enlu Zhou
Tuo Zhao
62
1
0
07 Feb 2022
Finite-Sum Optimization: A New Perspective for Convergence to a Global
  Solution
Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution
Lam M. Nguyen
Trang H. Tran
Marten van Dijk
102
3
0
07 Feb 2022
Demystify Optimization and Generalization of Over-parameterized
  PAC-Bayesian Learning
Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning
Wei Huang
Chunrui Liu
Yilan Chen
Tianyu Liu
R. Xu
BDLMLT
67
2
0
04 Feb 2022
Improved Overparametrization Bounds for Global Convergence of Stochastic
  Gradient Descent for Shallow Neural Networks
Improved Overparametrization Bounds for Global Convergence of Stochastic Gradient Descent for Shallow Neural Networks
Bartlomiej Polaczyk
J. Cyranka
ODL
61
3
0
28 Jan 2022
Convergence of Invariant Graph Networks
Convergence of Invariant Graph Networks
Chen Cai
Yusu Wang
109
4
0
25 Jan 2022
STOPS: Short-Term-based Volatility-controlled Policy Search and its
  Global Convergence
STOPS: Short-Term-based Volatility-controlled Policy Search and its Global Convergence
Liang Xu
Daoming Lyu
Yangchen Pan
Aiwen Jiang
Bo Liu
95
0
0
24 Jan 2022
How does unlabeled data improve generalization in self-training? A
  one-hidden-layer theoretical analysis
How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
Shuai Zhang
Ming Wang
Sijia Liu
Pin-Yu Chen
Jinjun Xiong
SSLMLT
111
23
0
21 Jan 2022
Accelerated Gradient Flow: Risk, Stability, and Implicit Regularization
Accelerated Gradient Flow: Risk, Stability, and Implicit Regularization
Yue Sheng
Alnur Ali
122
2
0
20 Jan 2022
Learning Neural Ranking Models Online from Implicit User Feedback
Learning Neural Ranking Models Online from Implicit User Feedback
Yiling Jia
Hongning Wang
94
6
0
17 Jan 2022
Towards Sample-efficient Overparameterized Meta-learning
Towards Sample-efficient Overparameterized Meta-learning
Yue Sun
Adhyyan Narang
Halil Ibrahim Gulluk
Samet Oymak
Maryam Fazel
BDL
68
25
0
16 Jan 2022
Largest Eigenvalues of the Conjugate Kernel of Single-Layered Neural
  Networks
Largest Eigenvalues of the Conjugate Kernel of Single-Layered Neural Networks
L. Benigni
Sandrine Péché
83
8
0
13 Jan 2022
Implicit Bias of MSE Gradient Optimization in Underparameterized Neural
  Networks
Implicit Bias of MSE Gradient Optimization in Underparameterized Neural Networks
Benjamin Bowman
Guido Montúfar
106
11
0
12 Jan 2022
Neural calibration of hidden inhomogeneous Markov chains -- Information
  decompression in life insurance
Neural calibration of hidden inhomogeneous Markov chains -- Information decompression in life insurance
Mark Kiermayer
Christian Weiß
38
0
0
07 Jan 2022
Efficient Global Optimization of Two-Layer ReLU Networks: Quadratic-Time Algorithms and Adversarial Training
Efficient Global Optimization of Two-Layer ReLU Networks: Quadratic-Time Algorithms and Adversarial Training
Yatong Bai
Tanmay Gautam
Somayeh Sojoudi
AAML
112
17
0
06 Jan 2022
Stochastic Continuous Submodular Maximization: Boosting via
  Non-oblivious Function
Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function
Qixin Zhang
Zengde Deng
Zaiyi Chen
Haoyuan Hu
Yu Yang
84
20
0
03 Jan 2022
A Theoretical View of Linear Backpropagation and Its Convergence
A Theoretical View of Linear Backpropagation and Its Convergence
Ziang Li
Yiwen Guo
Haodi Liu
Changshui Zhang
AAML
41
4
0
21 Dec 2021
Rethinking Influence Functions of Neural Networks in the
  Over-parameterized Regime
Rethinking Influence Functions of Neural Networks in the Over-parameterized Regime
Rui Zhang
Shihua Zhang
TDI
70
23
0
15 Dec 2021
Decomposing the Deep: Finding Class Specific Filters in Deep CNNs
Decomposing the Deep: Finding Class Specific Filters in Deep CNNs
Akshay Badola
Cherian Roy
V. Padmanabhan
R. Lal
FAtt
64
2
0
14 Dec 2021
Training Multi-Layer Over-Parametrized Neural Network in Subquadratic
  Time
Training Multi-Layer Over-Parametrized Neural Network in Subquadratic Time
Zhao Song
Licheng Zhang
Ruizhe Zhang
116
66
0
14 Dec 2021
SCORE: Approximating Curvature Information under Self-Concordant
  Regularization
SCORE: Approximating Curvature Information under Self-Concordant Regularization
Adeyemi Damilare Adeoye
Alberto Bemporad
44
4
0
14 Dec 2021
Eigenspace Restructuring: a Principle of Space and Frequency in Neural
  Networks
Eigenspace Restructuring: a Principle of Space and Frequency in Neural Networks
Lechao Xiao
110
22
0
10 Dec 2021
Global convergence of ResNets: From finite to infinite width using
  linear parameterization
Global convergence of ResNets: From finite to infinite width using linear parameterization
Raphael Barboni
Gabriel Peyré
Franccois-Xavier Vialard
68
12
0
10 Dec 2021
Provable Continual Learning via Sketched Jacobian Approximations
Provable Continual Learning via Sketched Jacobian Approximations
Reinhard Heckel
CLL
78
10
0
09 Dec 2021
Understanding Square Loss in Training Overparametrized Neural Network
  Classifiers
Understanding Square Loss in Training Overparametrized Neural Network Classifiers
Tianyang Hu
Jun Wang
Wei Cao
Zhenguo Li
UQCVAAML
92
19
0
07 Dec 2021
On the Convergence of Shallow Neural Network Training with Randomly
  Masked Neurons
On the Convergence of Shallow Neural Network Training with Randomly Masked Neurons
Fangshuo Liao
Anastasios Kyrillidis
120
16
0
05 Dec 2021
Fast Graph Neural Tangent Kernel via Kronecker Sketching
Fast Graph Neural Tangent Kernel via Kronecker Sketching
Shunhua Jiang
Yunze Man
Zhao Song
Zheng Yu
Danyang Zhuo
96
6
0
04 Dec 2021
Pixelated Butterfly: Simple and Efficient Sparse training for Neural
  Network Models
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models
Tri Dao
Beidi Chen
Kaizhao Liang
Jiaming Yang
Zhao Song
Atri Rudra
Christopher Ré
133
79
0
30 Nov 2021
Generalization Performance of Empirical Risk Minimization on
  Over-parameterized Deep ReLU Nets
Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets
Shao-Bo Lin
Yao Wang
Ding-Xuan Zhou
ODL
92
6
0
28 Nov 2021
Offline Neural Contextual Bandits: Pessimism, Optimization and
  Generalization
Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization
Thanh Nguyen-Tang
Sunil R. Gupta
A. Nguyen
Svetha Venkatesh
OffRL
97
30
0
27 Nov 2021
KNAS: Green Neural Architecture Search
KNAS: Green Neural Architecture Search
Jingjing Xu
Liang Zhao
Junyang Lin
Rundong Gao
Xu Sun
Hongxia Yang
78
59
0
26 Nov 2021
Modeling Design and Control Problems Involving Neural Network Surrogates
Modeling Design and Control Problems Involving Neural Network Surrogates
Dominic Yang
Prasanna Balaprakash
S. Leyffer
52
15
0
20 Nov 2021
Benchmarking and scaling of deep learning models for land cover image
  classification
Benchmarking and scaling of deep learning models for land cover image classification
Ioannis Papoutsis
Nikolaos Ioannis Bountos
Angelos Zavras
Dimitrios Michail
Christos Tryfonopoulos
108
61
0
18 Nov 2021
Learning with convolution and pooling operations in kernel methods
Learning with convolution and pooling operations in kernel methods
Theodor Misiakiewicz
Song Mei
MLT
90
29
0
16 Nov 2021
The Three Stages of Learning Dynamics in High-Dimensional Kernel Methods
The Three Stages of Learning Dynamics in High-Dimensional Kernel Methods
Nikhil Ghosh
Song Mei
Bin Yu
73
20
0
13 Nov 2021
On the Equivalence between Neural Network and Support Vector Machine
On the Equivalence between Neural Network and Support Vector Machine
Yilan Chen
Wei Huang
Lam M. Nguyen
Tsui-Wei Weng
AAML
73
18
0
11 Nov 2021
Theoretical Exploration of Flexible Transmitter Model
Theoretical Exploration of Flexible Transmitter Model
Jin-Hui Wu
Shao-Qun Zhang
Yuan Jiang
Zhiping Zhou
63
3
0
11 Nov 2021
Accelerated replica exchange stochastic gradient Langevin diffusion
  enhanced Bayesian DeepONet for solving noisy parametric PDEs
Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs
Guang Lin
Christian Moya
Zecheng Zhang
76
30
0
03 Nov 2021
Subquadratic Overparameterization for Shallow Neural Networks
Subquadratic Overparameterization for Shallow Neural Networks
Chaehwan Song
Ali Ramezani-Kebrya
Thomas Pethick
Armin Eftekhari
Volkan Cevher
81
31
0
02 Nov 2021
Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity
  Bias
Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias
Kaifeng Lyu
Zhiyuan Li
Runzhe Wang
Sanjeev Arora
MLT
110
76
0
26 Oct 2021
Quantifying Epistemic Uncertainty in Deep Learning
Quantifying Epistemic Uncertainty in Deep Learning
Ziyi Huang
Henry Lam
Haofeng Zhang
UQCVBDLUDPER
134
14
0
23 Oct 2021
UniFed: A Unified Framework for Federated Learning on Non-IID Image
  Features
UniFed: A Unified Framework for Federated Learning on Non-IID Image Features
Meirui Jiang
Xiaoxiao Li
Xiaofei Zhang
Michael Kamp
Qianming Dou
FedMLOOD
100
0
0
19 Oct 2021
On Reward-Free RL with Kernel and Neural Function Approximations:
  Single-Agent MDP and Markov Game
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game
Shuang Qiu
Jieping Ye
Zhaoran Wang
Zhuoran Yang
OffRL
104
23
0
19 Oct 2021
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