Approximate Inference Turns Deep Networks into Gaussian Processes

5 June 2019

Mohammad Emtiyaz Khan

Papers citing "Approximate Inference Turns Deep Networks into Gaussian Processes"

50 / 85 papers shown

Title
Confidence Sequences for Generalized Linear Models via Regret Analysis Eugenio Clerico Hamish Flynn W. Kotłowski Gergely Neu 29 0 0 23 Apr 2025
Efficient Membership Inference Attacks by Bayesian Neural Network Zhenlong Liu Wenyu Jiang Feng Zhou Hongxin Wei MIALM 71 1 0 10 Mar 2025
Pareto Set Learning for Multi-Objective Reinforcement Learning Erlong Liu Yu-Chang Wu Xiaobin Huang Chengrui Gao Ren-Jian Wang Ke Xue Chao Qian OffRL 42 2 0 12 Jan 2025
Active Fine-Tuning of Generalist Policies Marco Bagatella Jonas Hübotter Georg Martius Andreas Krause 32 0 0 07 Oct 2024
Realistic Extreme Behavior Generation for Improved AV Testing Robert Dyro Matthew Foutter Ruolin Li L. D. Lillo Edward Schmerling Xilin Zhou Marco Pavone AAML 33 1 0 16 Sep 2024
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection Steven Adams A. Patané Morteza Lahijanian Luca Laurenti BDL 36 2 0 26 Jul 2024
Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani Marvin Pfortner Tobias Weber Philipp Hennig UQCV 66 1 0 07 Jun 2024
Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks Tristan Cinquin Robert Bamler UQCV BDL 43 2 0 06 Jun 2024
Reparameterization invariance in approximate Bayesian inference Hrittik Roy M. Miani Carl Henrik Ek Philipp Hennig Marvin Pfortner Lukas Tatzel Søren Hauberg BDL 47 8 0 05 Jun 2024
Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models Emre Onal Klemens Flöge Emma Caldwell A. Sheverdin Vincent Fortuin UQCV BDL 45 9 0 06 May 2024
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction Ubaid Azam Imran Razzak Shelly Vishwakarma Hakim Hacid Dell Zhang Shoaib Jameel UQCV ELM BDL 32 0 0 16 Apr 2024
Function-space Parameterization of Neural Networks for Sequential Learning Aidan Scannell Riccardo Mereu Paul E. Chang Ella Tamir Joni Pajarinen Arno Solin BDL 34 5 0 16 Mar 2024
Continual Learning and Catastrophic Forgetting Gido M. van de Ven Nicholas Soures Dhireesha Kudithipudi 32 48 0 08 Mar 2024
A prediction rigidity formalism for low-cost uncertainties in trained neural networks Filippo Bigi Sanggyu Chong Michele Ceriotti Federico Grasselli 49 5 0 04 Mar 2024
Active Few-Shot Fine-Tuning Jonas Hübotter Bhavya Sukhija Lenart Treven Yarden As Andreas Krause 42 1 0 13 Feb 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel ... David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang UQCV BDL 40 27 0 01 Feb 2024
Tractable Function-Space Variational Inference in Bayesian Neural Networks Tim G. J. Rudner Zonghao Chen Yee Whye Teh Y. Gal 80 39 0 28 Dec 2023
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks Alexander M¨ollers Alexander Immer Elvin Isufi Vincent Fortuin SSL BDL UQCV 21 1 0 30 Nov 2023
Federated Transformed Learning for a Circular, Secure, and Tiny AI Weisi Guo S. Sun Bin Li Sam Blakeman 22 0 0 24 Nov 2023
The Memory Perturbation Equation: Understanding Model's Sensitivity to Data Peter Nickl Lu Xu Dharmesh Tailor Thomas Möllenhoff Mohammad Emtiyaz Khan 24 10 0 30 Oct 2023
On permutation symmetries in Bayesian neural network posteriors: a variational perspective Simone Rossi Ankit Singh T. Hannagan 32 2 0 16 Oct 2023
On the Disconnect Between Theory and Practice of Neural Networks: Limits of the NTK Perspective Jonathan Wenger Felix Dangel Agustinus Kristiadi 33 0 0 29 Sep 2023
A Primer on Bayesian Neural Networks: Review and Debates Federico Danieli Konstantinos Pitas M. Vladimirova Vincent Fortuin BDL AAML 56 18 0 28 Sep 2023
Sparse Function-space Representation of Neural Networks Aidan Scannell Riccardo Mereu Paul E. Chang Ella Tamir Joni Pajarinen Arno Solin BDL 35 1 0 05 Sep 2023
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks Dominik Schnaus Jongseok Lee Daniel Cremers Rudolph Triebel UQCV BDL 38 1 0 15 Jul 2023
Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations Wyatt Bridgman Reese E. Jones Mohammad Khalil 29 1 0 12 Jul 2023
Online Laplace Model Selection Revisited J. Lin Javier Antorán José Miguel Hernández-Lobato BDL 32 3 0 12 Jul 2023
Function-Space Regularization for Deep Bayesian Classification J. Lin Joe Watson Pascal Klink Jan Peters UQCV BDL 38 1 0 12 Jul 2023
Riemannian Laplace approximations for Bayesian neural networks Federico Bergamin Pablo Moreno-Muñoz Søren Hauberg Georgios Arvanitidis BDL 35 6 0 12 Jun 2023
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels Alexander Immer Tycho F. A. van der Ouderaa Mark van der Wilk Gunnar Rätsch Bernhard Schölkopf BDL 31 11 0 06 Jun 2023
Improving Neural Additive Models with Bayesian Principles Kouroche Bouchiat Alexander Immer Hugo Yèche Gunnar Rätsch Vincent Fortuin BDL MedIm 31 6 0 26 May 2023
Uncertainty and Structure in Neural Ordinary Differential Equations Katharina Ott Michael Tiemann Philipp Hennig AI4CE 26 5 0 22 May 2023
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization Agustinus Kristiadi Alexander Immer Runa Eschenhagen Vincent Fortuin BDL UQCV 17 8 0 17 Apr 2023
Bayesian inference with finitely wide neural networks Chi-Ken Lu BDL 37 0 0 06 Mar 2023
Variational Linearized Laplace Approximation for Bayesian Deep Learning Luis A. Ortega Simón Rodríguez Santana Daniel Hernández-Lobato BDL UQCV 47 4 0 24 Feb 2023
Guided Deep Kernel Learning Idan Achituve Gal Chechik Ethan Fetaya BDL 31 5 0 19 Feb 2023
Probabilistic Circuits That Know What They Don't Know Fabrizio G. Ventola Steven Braun Zhongjie Yu Martin Mundt Kristian Kersting UQCV TPM 32 7 0 13 Feb 2023
M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-Distortion Yangyi Liu Stefano Rini Sadaf Salehkalaibar Jun Chen FedML 21 4 0 23 Jan 2023
Introduction and Exemplars of Uncertainty Decomposition Shuo Chen UD UQCV PER 30 0 0 17 Nov 2022
The Implicit Delta Method Nathan Kallus James McInerney 23 1 0 11 Nov 2022
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning Zhijie Deng Feng Zhou Jun Zhu BDL 47 19 0 23 Oct 2022
Bridging the Gap Between Target Networks and Functional Regularization Alexandre Piché Valentin Thomas Joseph Marino Rafael Pardiñas Gian Maria Marconi C. Pal Mohammad Emtiyaz Khan 14 1 0 21 Oct 2022
Sampling-based inference for large linear models, with application to linearised Laplace Javier Antorán Shreyas Padhy Riccardo Barbano Eric T. Nalisnick David Janz José Miguel Hernández-Lobato BDL 27 17 0 10 Oct 2022
Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel Sungyub Kim Si-hun Park Kyungsu Kim Eunho Yang BDL 26 4 0 30 Sep 2022
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs Emilia Magnani Nicholas Kramer Runa Eschenhagen Lorenzo Rosasco Philipp Hennig UQCV BDL 15 9 0 02 Aug 2022
Cold Posteriors through PAC-Bayes Konstantinos Pitas Julyan Arbel 23 5 0 22 Jun 2022
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning Javier Antorán David Janz J. Allingham Erik A. Daxberger Riccardo Barbano Eric T. Nalisnick José Miguel Hernández-Lobato UQCV BDL 27 28 0 17 Jun 2022
Fast Finite Width Neural Tangent Kernel Roman Novak Jascha Narain Sohl-Dickstein S. Schoenholz AAML 20 53 0 17 Jun 2022
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness J. Liu Shreyas Padhy Jie Jessie Ren Zi Lin Yeming Wen Ghassen Jerfel Zachary Nado Jasper Snoek Dustin Tran Balaji Lakshminarayanan UQCV BDL 21 48 0 01 May 2022
NeuralEF: Deconstructing Kernels by Deep Neural Networks Zhijie Deng Jiaxin Shi Jun Zhu 24 18 0 30 Apr 2022