v1v2 (latest)

Posterior and variational inference for deep neural networks with heavy-tailed weights

5 June 2024

Papers citing "Posterior and variational inference for deep neural networks with heavy-tailed weights"

30 / 30 papers shown

Title
Variational Bayesian Bow tie Neural Networks with Shrinkage Alisa Sheinkman Sara Wade BDL UQCV 85 0 0 17 Nov 2024
Minimax optimality of deep neural networks on dependent data via PAC-Bayes bounds Pierre Alquier William Kengne 77 0 0 29 Oct 2024
Posterior concentrations of fully-connected Bayesian neural networks with general priors on the weights Insung Kong Yongdai Kim UQCV BDL 63 2 0 21 Mar 2024
Contraction rates and projection subspace estimation with Gaussian process priors in high dimension Elie Odin François Bachoc A. Lagnoux 80 0 0 06 Mar 2024
Deep Horseshoe Gaussian Processes Ismael Castillo Thibault Randrianarisoa BDL UQCV 90 5 0 04 Mar 2024
A Primer on Bayesian Neural Networks: Review and Debates Federico Danieli Konstantinos Pitas M. Vladimirova Vincent Fortuin BDL AAML 88 20 0 28 Sep 2023
Heavy-tailed Bayesian nonparametric adaptation S. Agapiou I. Castillo 51 7 0 09 Aug 2023
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference Insung Kong Dongyoon Yang Jongjin Lee Ilsang Ohn Gyuseung Baek Yongdai Kim BDL 66 5 0 24 May 2023
$Semiparametric inference using fractional posteriors$ Semiparametric inference using fractional posteriors Alice L'Huillier Luke Travis I. Castillo Kolyan Ray 36 5 0 19 Jan 2023
Asymptotic Properties for Bayesian Neural Network in Besov Space Kyeongwon Lee Jaeyong Lee BDL 74 4 0 01 Jun 2022
User-friendly introduction to PAC-Bayes bounds Pierre Alquier FedML 154 206 0 21 Oct 2021
Adaptive variational Bayes: Optimality, computation and applications Ilsang Ohn Lizhen Lin 51 12 0 07 Sep 2021
Posterior contraction for deep Gaussian process priors G. Finocchio Johannes Schmidt-Hieber 82 11 0 16 May 2021
Deep Nonparametric Regression on Approximate Manifolds: Non-Asymptotic Error Bounds with Polynomial Prefactors Yuling Jiao Guohao Shen Yuanyuan Lin Jian Huang 85 52 0 14 Apr 2021
Trace-class Gaussian priors for Bayesian learning of neural networks with MCMC Torben Sell Sumeetpal S. Singh BDL 120 5 0 20 Dec 2020
Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee Jincheng Bai Qifan Song Guang Cheng BDL 40 40 0 15 Nov 2020
Deep Network Approximation for Smooth Functions Jianfeng Lu Zuowei Shen Haizhao Yang Shijun Zhang 116 248 0 09 Jan 2020
Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space Taiji Suzuki Atsushi Nitanda 59 63 0 28 Oct 2019
Convergence Rates of Variational Inference in Sparse Deep Learning Badr-Eddine Chérief-Abdellatif BDL 83 39 0 09 Aug 2019
Deep ReLU network approximation of functions on a manifold Johannes Schmidt-Hieber 87 95 0 02 Aug 2019
Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality Taiji Suzuki 182 246 0 18 Oct 2018
Posterior Concentration for Sparse Deep Learning Nicholas G. Polson Veronika Rockova UQCV BDL 178 90 0 24 Mar 2018
Convergence Rates of Variational Posterior Distributions Fengshuo Zhang Chao Gao 63 105 0 07 Dec 2017
Nonparametric regression using deep neural networks with ReLU activation function Johannes Schmidt-Hieber 238 815 0 22 Aug 2017
Concentration of tempered posteriors and of their variational approximations Pierre Alquier James Ridgway 84 126 0 28 Jun 2017
Model Selection in Bayesian Neural Networks via Horseshoe Priors S. Ghosh Finale Doshi-Velez BDL 67 120 0 29 May 2017
$Bayesian fractional posteriors$ Bayesian fractional posteriors A. Bhattacharya D. Pati Yun Yang 80 110 0 03 Nov 2016
Error bounds for approximations with deep ReLU networks Dmitry Yarotsky 198 1,233 0 03 Oct 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 298 4,807 0 04 Jan 2016
Deep Gaussian Processes Andreas C. Damianou Neil D. Lawrence GP BDL 146 1,184 0 02 Nov 2012