Taming neural networks with TUSLA: Non-convex learning via adaptive
stochastic gradient Langevin algorithms

Taming neural networks with TUSLA: Non-convex learning via adaptive stochastic gradient Langevin algorithms

25 June 2020

Miklós Rásonyi

Sotirios Sabanis

Papers citing "Taming neural networks with TUSLA: Non-convex learning via adaptive stochastic gradient Langevin algorithms"

12 / 12 papers shown

Title
Non-convex sampling for a mixture of locally smooth potentials D. Nguyen 38 0 0 31 Jan 2023
Kinetic Langevin MCMC Sampling Without Gradient Lipschitz Continuity -- the Strongly Convex Case Tim Johnston Iosif Lytras Sotirios Sabanis 43 8 0 19 Jan 2023
Non-asymptotic convergence bounds for modified tamed unadjusted Langevin algorithm in non-convex setting Ariel Neufeld Matthew Ng Cheng En Ying Zhang 39 11 0 06 Jul 2022
Unadjusted Langevin algorithm for sampling a mixture of weakly smooth potentials D. Nguyen 40 5 0 17 Dec 2021
Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions Martin Hutzenthaler Arnulf Jentzen Katharina Pohl Adrian Riekert Luca Scarpa MLT 38 6 0 13 Dec 2021
Statistical Finite Elements via Langevin Dynamics Ömer Deniz Akyildiz Connor Duffin Sotirios Sabanis Mark Girolami 41 11 0 21 Oct 2021
A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions Arnulf Jentzen Adrian Riekert 40 13 0 10 Aug 2021
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks Dong-Young Lim Sotirios Sabanis 44 11 0 28 May 2021
A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions Arnulf Jentzen Adrian Riekert MLT 42 13 0 01 Apr 2021
Convergence rates for gradient descent in the training of overparameterized artificial neural networks with biases Arnulf Jentzen T. Kröger ODL 35 7 0 23 Feb 2021
A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions Patrick Cheridito Arnulf Jentzen Adrian Riekert Florian Rossmannek 32 24 0 19 Feb 2021
Weak error analysis for stochastic gradient descent optimization algorithms A. Bercher Lukas Gonon Arnulf Jentzen Diyora Salimova 36 4 0 03 Jul 2020