Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods

19 July 2018

Papers citing "Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods"

11 / 11 papers shown

Title
Are vision language models robust to uncertain inputs? Xi Wang Eric Nalisnick AAML VLM Presented at ResearchTrend Connect \| VLM on 18 Jun 2025 144 1 0 17 May 2025
Implicit Maximum a Posteriori Filtering via Adaptive Optimization Gianluca M Bencomo Jake C. Snell Thomas L. Griffiths 419 4 0 17 Nov 2023
On Sequential Bayesian Inference for Continual Learning Samuel Kessler Adam D. Cobb Tim G. J. Rudner S. Zohren Stephen J. Roberts CLL BDL 116 7 0 04 Jan 2023
Hebbian Deep Learning Without Feedback Adrien Journé Hector Garcia Rodriguez Qinghai Guo Timoleon Moraitis AAML 91 54 0 23 Sep 2022
A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies Adam Thelen Xiaoge Zhang Olga Fink Yan Lu Sayan Ghosh B. Youn Michael D. Todd S. Mahadevan Chao Hu Zhen Hu SyDa AI4CE 92 207 0 26 Aug 2022
Robustness to corruption in pre-trained Bayesian neural networks Xi Wang Laurence Aitchison OOD UQCV 54 5 0 24 Jun 2022
Gradient Descent on Neurons and its Link to Approximate Second-Order Optimization Frederik Benzing ODL 127 26 0 28 Jan 2022
The Bayesian Learning Rule Mohammad Emtiyaz Khan Håvard Rue BDL 159 83 0 09 Jul 2021
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers Robin M. Schmidt Frank Schneider Philipp Hennig ODL 215 168 0 03 Jul 2020
On Empirical Comparisons of Optimizers for Deep Learning Dami Choi Christopher J. Shallue Zachary Nado Jaehoon Lee Chris J. Maddison George E. Dahl 126 259 0 11 Oct 2019
A Latent Variational Framework for Stochastic Optimization P. Casgrain DRL 24 4 0 05 May 2019