The effective noise of Stochastic Gradient Descent

20 December 2021

Papers citing "The effective noise of Stochastic Gradient Descent"

9 / 9 papers shown

Title
Convergence, Sticking and Escape: Stochastic Dynamics Near Critical Points in SGD Dmitry Dudukalov Artem Logachov Vladimir Lotov Timofei Prasolov Evgeny Prokopenko Anton Tarasenko 42 0 0 24 May 2025
Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer Blake Bordelon Cengiz Pehlevan AI4CE 153 1 0 04 Feb 2025
Connecting NTK and NNGP: A Unified Theoretical Framework for Wide Neural Network Learning Dynamics Yehonatan Avidan Qianyi Li H. Sompolinsky 92 8 0 08 Sep 2023
Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem Francesca Mignacco Pierfrancesco Urbani Lenka Zdeborová 62 36 0 08 Mar 2021
Poly-time universality and limitations of deep learning Emmanuel Abbe Colin Sandon 40 23 0 07 Jan 2020
Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning Baoyuan Wu Weidong Chen Yanbo Fan Yong Zhang Jinlong Hou Jie Liu Tong Zhang VLM MLLM 62 86 0 07 Jan 2019
A Mean Field View of the Landscape of Two-Layers Neural Networks Song Mei Andrea Montanari Phan-Minh Nguyen MLT 91 858 0 18 Apr 2018
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 424 2,937 0 15 Sep 2016
Representation Learning: A Review and New Perspectives Yoshua Bengio Aaron Courville Pascal Vincent OOD SSL 261 12,439 0 24 Jun 2012