Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam

13 June 2018

Mohammad Emtiyaz Khan

Voot Tangkaratt

Akash Srivastava

Papers citing "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam"

18 / 18 papers shown

Title
Spectral-factorized Positive-definite Curvature Learning for NN Training Wu Lin Felix Dangel Runa Eschenhagen Juhan Bae Richard E. Turner Roger B. Grosse 34 0 0 10 Feb 2025
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics Daniel Paulin P. Whalley Neil K. Chada B. Leimkuhler BDL 24 4 0 14 Oct 2024
Manifold Sampling for Differentiable Uncertainty in Radiance Fields Linjie Lyu Ayush Tewari Marc Habermann Shunsuke Saito Michael Zollhöfer Thomas Leimkühler Christian Theobalt UQCV 18 1 0 19 Sep 2024
Function-Space MCMC for Bayesian Wide Neural Networks Lucia Pezzetti Stefano Favaro Stefano Peluchetti BDL 26 0 0 26 Aug 2024
Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani Marvin Pfortner Tobias Weber Philipp Hennig UQCV 45 1 0 07 Jun 2024
WeiPer: OOD Detection using Weight Perturbations of Class Projections Maximilian Granz Manuel Heurich Tim Landgraf OODD 27 1 0 27 May 2024
Variational Stochastic Gradient Descent for Deep Neural Networks Haotian Chen Anna Kuzina Babak Esmaeili Jakub M. Tomczak 27 0 0 09 Apr 2024
Model Merging by Uncertainty-Based Gradient Matching Nico Daheim Thomas Möllenhoff E. Ponti Iryna Gurevych Mohammad Emtiyaz Khan MoMe FedML 19 43 0 19 Oct 2023
Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference Arnaud Descours Tom Huix Arnaud Guillin Manon Michel Eric Moulines Boris Nectoux BDL 4 1 0 10 Jul 2023
Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHS Rishabh Singh José C. Príncipe UQCV 8 3 0 03 Nov 2022
Optimization for Amortized Inverse Problems Tianci Liu Tong Yang Quan Zhang Qi Lei 8 4 0 25 Oct 2022
Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift Andreas Klass Sven M. Lorenz M. Lauer-Schmaltz David Rügamer Bernd Bischl Christopher Mutschler Felix Ott 19 10 0 17 Jun 2022
Stochastic Neural Networks with Infinite Width are Deterministic Liu Ziyin Hanlin Zhang Xiangming Meng Yuting Lu Eric P. Xing Masakuni Ueda 6 3 0 30 Jan 2022
Gradient Descent on Neurons and its Link to Approximate Second-Order Optimization Frederik Benzing ODL 29 23 0 28 Jan 2022
Bayesian Image Reconstruction using Deep Generative Models Razvan V. Marinescu Daniel Moyer Polina Golland OOD DiffM 10 37 0 08 Dec 2020
The Emerging Trends of Multi-Label Learning Weiwei Liu Haobo Wang Xiaobo Shen Ivor W. Tsang 25 244 0 23 Nov 2020
Efficient Per-Example Gradient Computations Ian Goodfellow 140 73 0 07 Oct 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 243 9,042 0 06 Jun 2015