A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network

4 October 2020

Papers citing "A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network"

9 / 9 papers shown

Title
A Nonstochastic Control Approach to Optimization Xinyi Chen Elad Hazan 49 5 0 19 Jan 2023
Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation Jun-Kun Wang Andre Wibisono 37 7 0 18 Oct 2022
FedFOR: Stateless Heterogeneous Federated Learning with First-Order Regularization Junjiao Tian James Smith Z. Kira 27 3 0 21 Sep 2022
Deep Linear Networks can Benignly Overfit when Shallow Ones Do Niladri S. Chatterji Philip M. Long 28 8 0 19 Sep 2022
Provable Acceleration of Nesterov's Accelerated Gradient Method over Heavy Ball Method in Training Over-Parameterized Neural Networks Xin Liu Wei Tao Wei Li Dazhi Zhan Jun Wang Zhisong Pan ODL 32 1 0 08 Aug 2022
Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time Jun-Kun Wang Andre Wibisono 32 9 0 05 Jul 2022
Provable Acceleration of Heavy Ball beyond Quadratics for a Class of Polyak-Łojasiewicz Functions when the Non-Convexity is Averaged-Out Jun-Kun Wang Chi-Heng Lin Andre Wibisono Bin Hu 38 20 0 22 Jun 2022
A new regret analysis for Adam-type algorithms Ahmet Alacaoglu Yura Malitsky P. Mertikopoulos V. Cevher ODL 48 42 0 21 Mar 2020
Global optimality conditions for deep neural networks Chulhee Yun S. Sra Ali Jadbabaie 128 118 0 08 Jul 2017