Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
1802.05155
Cited By
v1
v2
v3
v4
v5 (latest)
A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization
14 February 2018
Tianyi Liu
Zhehui Chen
Enlu Zhou
T. Zhao
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization"
8 / 8 papers shown
Accelerate Distributed Stochastic Descent for Nonconvex Optimization with Momentum
Guojing Cong
Tianyi Liu
270
1
0
01 Oct 2021
Dynamic of Stochastic Gradient Descent with State-Dependent Noise
Qi Meng
Shiqi Gong
Wei Chen
Zhi-Ming Ma
Tie-Yan Liu
370
16
0
24 Jun 2020
Rethinking the Hyperparameters for Fine-tuning
International Conference on Learning Representations (ICLR), 2020
Hao Li
Pratik Chaudhari
Hao Yang
Michael Lam
Avinash Ravichandran
Rahul Bhotika
Stefano Soatto
VLM
218
141
0
19 Feb 2020
Learning to Defend by Learning to Attack
Haoming Jiang
Zhehui Chen
Yuyang Shi
Bo Dai
T. Zhao
329
24
0
03 Nov 2018
Accelerating SGD with momentum for over-parameterized learning
Chaoyue Liu
M. Belkin
ODL
432
19
0
31 Oct 2018
Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration
Ningyuan Chen
Mert Gurbuzbalaban
Lingjiong Zhu
429
69
0
12 Sep 2018
Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization
Tianyi Liu
Shiyang Li
Jianping Shi
Enlu Zhou
T. Zhao
288
10
0
04 Jun 2018
A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay
L. Smith
788
1,159
0
26 Mar 2018
1
Page 1 of 1
