Mean-Field Analysis of Two-Layer Neural Networks: Global Optimality with
Linear Convergence Rates

Mean-Field Analysis of Two-Layer Neural Networks: Global Optimality with Linear Convergence Rates

19 May 2022

Papers citing "Mean-Field Analysis of Two-Layer Neural Networks: Global Optimality with Linear Convergence Rates"

11 / 11 papers shown

Title
Convex Analysis of the Mean Field Langevin Dynamics Atsushi Nitanda Denny Wu Taiji Suzuki MLT 84 65 0 25 Jan 2022
Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks Cong Fang Jason D. Lee Pengkun Yang Tong Zhang OOD FedML 130 57 0 03 Jul 2020
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence Sinho Chewi Thibaut Le Gouic Chen Lu Tyler Maunu Philippe Rigollet 85 70 0 03 Jun 2020
Feature Purification: How Adversarial Training Performs Robust Deep Learning Zeyuan Allen-Zhu Yuanzhi Li MLT AAML 63 150 0 20 May 2020
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers Zeyuan Allen-Zhu Yuanzhi Li Yingyu Liang MLT 183 769 0 12 Nov 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 214 1,272 0 04 Oct 2018
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data Yuanzhi Li Yingyu Liang MLT 216 653 0 03 Aug 2018
Deep Neural Networks as Gaussian Processes Jaehoon Lee Yasaman Bahri Roman Novak S. Schoenholz Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 115 1,093 0 01 Nov 2017
Stein Variational Gradient Descent as Gradient Flow Qiang Liu OT 72 275 0 25 Apr 2017
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis Maxim Raginsky Alexander Rakhlin Matus Telgarsky 70 521 0 13 Feb 2017
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 65 1,092 0 16 Aug 2016