Incremental Gauss-Newton Descent for Machine Learning

10 August 2024

Papers citing "Incremental Gauss-Newton Descent for Machine Learning"

11 / 11 papers shown

Title
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization Quoc Tran-Dinh Nhan H. Pham Lam M. Nguyen 52 24 0 17 Feb 2020
Demon: Improved Neural Network Training with Momentum Decay John Chen Cameron R. Wolfe Zhaoqi Li Anastasios Kyrillidis ODL 68 15 0 11 Oct 2019
A Survey of Optimization Methods from a Machine Learning Perspective Shiliang Sun Zehui Cao Han Zhu Jing Zhao 56 558 0 17 Jun 2019
Why gradient clipping accelerates training: A theoretical justification for adaptivity J.N. Zhang Tianxing He S. Sra Ali Jadbabaie 76 467 0 28 May 2019
An Introduction to Deep Reinforcement Learning Vincent François-Lavet Peter Henderson Riashat Islam Marc G. Bellemare Joelle Pineau OffRL AI4CE 150 1,263 0 30 Nov 2018
Convergence of Gradient Descent on Separable Data Mor Shpigel Nacson Jason D. Lee Suriya Gunasekar Pedro H. P. Savarese Nathan Srebro Daniel Soudry 76 169 0 05 Mar 2018
Practical Gauss-Newton Optimisation for Deep Learning Aleksandar Botev H. Ritter David Barber ODL 61 232 0 12 Jun 2017
Optimizing Neural Networks with Kronecker-factored Approximate Curvature James Martens Roger C. Grosse ODL 104 1,023 0 19 Mar 2015
Training Neural Networks with Stochastic Hessian-Free Optimization Ryan Kiros BDL 100 48 0 16 Jan 2013
ADADELTA: An Adaptive Learning Rate Method Matthew D. Zeiler ODL 163 6,630 0 22 Dec 2012
Krylov Subspace Descent for Deep Learning Oriol Vinyals Daniel Povey ODL 76 148 0 18 Nov 2011