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Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks
International Conference on Learning Representations (ICLR), 2019
3 October 2019
Yu Bai
Jason D. Lee
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Papers citing
"Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks"
30 / 80 papers shown
Sample Complexity and Overparameterization Bounds for Temporal Difference Learning with Neural Network Approximation
IEEE Transactions on Automatic Control (IEEE TAC), 2021
Semih Cayci
Siddhartha Satpathi
Niao He
F. I. R. Srikant
188
11
0
02 Mar 2021
Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis
Neural Information Processing Systems (NeurIPS), 2020
Atsushi Nitanda
Denny Wu
Taiji Suzuki
441
31
0
31 Dec 2020
Mathematical Models of Overparameterized Neural Networks
Proceedings of the IEEE (Proc. IEEE), 2020
Cong Fang
Hanze Dong
Tong Zhang
281
25
0
27 Dec 2020
Generalization bounds for deep learning
Guillermo Valle Pérez
A. Louis
BDL
264
48
0
07 Dec 2020
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods
International Conference on Learning Representations (ICLR), 2020
Taiji Suzuki
Shunta Akiyama
MLT
222
12
0
06 Dec 2020
On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces
Zhuoran Yang
Chi Jin
Zhaoran Wang
Mengdi Wang
Sai Li
216
18
0
09 Nov 2020
Train simultaneously, generalize better: Stability of gradient-based minimax learners
International Conference on Machine Learning (ICML), 2020
Farzan Farnia
Asuman Ozdaglar
194
53
0
23 Oct 2020
Beyond Lazy Training for Over-parameterized Tensor Decomposition
Xiang Wang
Chenwei Wu
Jason D. Lee
Tengyu Ma
Rong Ge
257
15
0
22 Oct 2020
How Important is the Train-Validation Split in Meta-Learning?
Yu Bai
Minshuo Chen
Pan Zhou
T. Zhao
Jason D. Lee
Sham Kakade
Haiquan Wang
Caiming Xiong
274
61
0
12 Oct 2020
A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network
International Conference on Machine Learning (ICML), 2020
Jun-Kun Wang
Chi-Heng Lin
Jacob D. Abernethy
603
24
0
04 Oct 2020
Deep Networks and the Multiple Manifold Problem
International Conference on Learning Representations (ICLR), 2020
Sam Buchanan
D. Gilboa
John N. Wright
451
42
0
25 Aug 2020
A Dynamical Central Limit Theorem for Shallow Neural Networks
Zhengdao Chen
Grant M. Rotskoff
Joan Bruna
Eric Vanden-Eijnden
234
30
0
21 Aug 2020
Finite Versus Infinite Neural Networks: an Empirical Study
Neural Information Processing Systems (NeurIPS), 2020
Jaehoon Lee
S. Schoenholz
Jeffrey Pennington
Ben Adlam
Lechao Xiao
Roman Novak
Jascha Narain Sohl-Dickstein
309
227
0
31 Jul 2020
Understanding Implicit Regularization in Over-Parameterized Single Index Model
Journal of the American Statistical Association (JASA), 2020
Jianqing Fan
Zhuoran Yang
Mengxin Yu
315
22
0
16 Jul 2020
Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK
Annual Conference Computational Learning Theory (COLT), 2020
Yuanzhi Li
Tengyu Ma
Hongyang R. Zhang
MLT
215
28
0
09 Jul 2020
A Revision of Neural Tangent Kernel-based Approaches for Neural Networks
Kyungsu Kim
A. Lozano
Eunho Yang
AAML
242
0
0
02 Jul 2020
Associative Memory in Iterated Overparameterized Sigmoid Autoencoders
Yibo Jiang
Cengiz Pehlevan
195
15
0
30 Jun 2020
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks
Wei Hu
Lechao Xiao
Ben Adlam
Jeffrey Pennington
194
69
0
25 Jun 2020
Towards Understanding Hierarchical Learning: Benefits of Neural Representations
Neural Information Processing Systems (NeurIPS), 2020
Minshuo Chen
Yu Bai
Jason D. Lee
T. Zhao
Huan Wang
Caiming Xiong
R. Socher
SSL
306
53
0
24 Jun 2020
On the Global Optimality of Model-Agnostic Meta-Learning
International Conference on Machine Learning (ICML), 2020
Lingxiao Wang
Qi Cai
Zhuoran Yang
Zhaoran Wang
193
47
0
23 Jun 2020
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime
Atsushi Nitanda
Taiji Suzuki
284
45
0
22 Jun 2020
Dynamically Stable Infinite-Width Limits of Neural Classifiers
Eugene Golikov
118
8
0
11 Jun 2020
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
Yufeng Zhang
Qi Cai
Zhuoran Yang
Yongxin Chen
Zhaoran Wang
OOD
MLT
679
11
0
08 Jun 2020
On Infinite-Width Hypernetworks
Etai Littwin
Tomer Galanti
Lior Wolf
Greg Yang
457
11
0
27 Mar 2020
Towards a General Theory of Infinite-Width Limits of Neural Classifiers
International Conference on Machine Learning (ICML), 2020
Eugene Golikov
AI4CE
126
9
0
12 Mar 2020
An Optimization and Generalization Analysis for Max-Pooling Networks
Alon Brutzkus
Amir Globerson
MLT
AI4CE
273
4
0
22 Feb 2020
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks
Zixiang Chen
Yuan Cao
Quanquan Gu
Tong Zhang
MLT
248
10
0
10 Feb 2020
Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width
Yu Bai
Ben Krause
Huan Wang
Caiming Xiong
R. Socher
182
22
0
10 Feb 2020
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?
International Conference on Learning Representations (ICLR), 2019
Zixiang Chen
Yuan Cao
Difan Zou
Quanquan Gu
326
129
0
27 Nov 2019
A Theoretical Analysis of Deep Q-Learning
Jianqing Fan
Zhuoran Yang
Yuchen Xie
Zhaoran Wang
585
704
0
01 Jan 2019
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