Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks

13 March 2017

Papers citing "Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks"

9 / 9 papers shown

Title
Application of Langevin Dynamics to Advance the Quantum Natural Gradient Optimization Algorithm Oleksandr Borysenko Mykhailo Bratchenko Ilya Lukin Mykola Luhanko Ihor Omelchenko Andrii Sotnikov Alessandro Lomi 52 0 0 17 Feb 2025
Provable Convergence and Limitations of Geometric Tempering for Langevin Dynamics Omar Chehab Anna Korba Austin Stromme Adrien Vacher 35 2 0 13 Oct 2024
Calibrating AI Models for Wireless Communications via Conformal Prediction K. Cohen Sangwoo Park Osvaldo Simeone S. Shamai 32 6 0 15 Dec 2022
A Survey of Uncertainty in Deep Neural Networks J. Gawlikowski Cedrique Rovile Njieutcheu Tassi Mohsin Ali Jongseo Lee Matthias Humt ... R. Roscher Muhammad Shahzad Wen Yang R. Bamler Xiaoxiang Zhu BDL UQCV OOD 59 1,111 0 07 Jul 2021
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction Wei Deng Qi Feng G. Karagiannis Guang Lin F. Liang 33 8 0 02 Oct 2020
Predicting the outputs of finite deep neural networks trained with noisy gradients Gadi Naveh Oded Ben-David H. Sompolinsky Z. Ringel 16 20 0 02 Apr 2020
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators Changyou Chen Nan Ding Lawrence Carin 37 158 0 21 Oct 2016
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 308 2,890 0 15 Sep 2016
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 183 1,185 0 30 Nov 2014