Laplace Redux -- Effortless Bayesian Deep Learning

28 June 2021

Papers citing "Laplace Redux -- Effortless Bayesian Deep Learning"

50 / 211 papers shown

Title
Be Bayesian by Attachments to Catch More Uncertainty Shiyu Shen Bin Pan Tianyang Shi Tao Li Zhenwei Shi UQCV 22 0 0 19 Oct 2023
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference Siqi Kou Lei Gan Dequan Wang Chongxuan Li Zhijie Deng BDL DiffM 12 7 0 17 Oct 2023
A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors Olivier Laurent Emanuel Aldea Gianni Franchi BDL UQCV 10 5 0 12 Oct 2023
Implicit Variational Inference for High-Dimensional Posteriors Anshuk Uppal Kristoffer Stensbo-Smidt Wouter Boomsma J. Frellsen BDL 10 1 0 10 Oct 2023
Something for (almost) nothing: Improving deep ensemble calibration using unlabeled data Konstantinos Pitas Julyan Arbel BDL UQCV FedML 19 0 0 04 Oct 2023
FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation Xiang Liu Liangxi Liu Feiyang Ye Yunheng Shen Xia Li Linshan Jiang Jialin Li 23 3 0 30 Sep 2023
On the Disconnect Between Theory and Practice of Neural Networks: Limits of the NTK Perspective Jonathan Wenger Felix Dangel Agustinus Kristiadi 20 0 0 29 Sep 2023
A Primer on Bayesian Neural Networks: Review and Debates Federico Danieli Konstantinos Pitas M. Vladimirova Vincent Fortuin BDL AAML 54 18 0 28 Sep 2023
Context-Aware Generative Models for Prediction of Aircraft Ground Tracks Nick Pepper George De Ath Marc Thomas Richard Everson T. Dodwell 19 0 0 26 Sep 2023
Bayesian sparsification for deep neural networks with Bayesian model reduction Dimitrije Marković K. Friston S. Kiebel BDL UQCV 23 1 0 21 Sep 2023
Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields Lily Goli Cody Reading Silvia Sellán Alec Jacobson Andrea Tagliasacchi BDL UQCV 21 59 0 06 Sep 2023
Sparse Function-space Representation of Neural Networks Aidan Scannell Riccardo Mereu Paul E. Chang Ella Tamir J. Pajarinen Arno Solin BDL 28 1 0 05 Sep 2023
Bayesian Low-rank Adaptation for Large Language Models Adam X. Yang Maxime Robeyns Xi Wang Laurence Aitchison AI4CE BDL 11 44 0 24 Aug 2023
Towards Accelerated Model Training via Bayesian Data Selection Zhijie Deng Peng Cui Jun Zhu 8 4 0 21 Aug 2023
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks Dominik Schnaus Jongseok Lee Daniel Cremers Rudolph Triebel UQCV BDL 33 1 0 15 Jul 2023
Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations Wyatt Bridgman Reese E. Jones Mohammad Khalil 13 1 0 12 Jul 2023
Quantification of Uncertainty with Adversarial Models Kajetan Schweighofer L. Aichberger Mykyta Ielanskyi G. Klambauer Sepp Hochreiter UQCV 21 13 0 06 Jul 2023
Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation Jung Hyun Ryu Jaeheyoung Jeon Jewoong Cho Myung-joo Kang MoMe 11 1 0 05 Jul 2023
Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift Florian Seligmann P. Becker Michael Volpp Gerhard Neumann UQCV 14 14 0 21 Jun 2023
Deep Gaussian Mixture Ensembles Yousef El-Laham Niccolò Dalmasso Elizabeth Fons Svitlana Vyetrenko BDL UQCV 12 2 0 12 Jun 2023
Riemannian Laplace approximations for Bayesian neural networks Federico Bergamin Pablo Moreno-Muñoz Søren Hauberg Georgios Arvanitidis BDL 17 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 20 11 0 06 Jun 2023
Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity Metod Jazbec J. Allingham Dan Zhang Eric T. Nalisnick 11 11 0 05 Jun 2023
Quantifying Representation Reliability in Self-Supervised Learning Models Young-Jin Park Hao Wang Shervin Ardeshir Navid Azizan SSL UQCV 14 3 0 31 May 2023
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization Y. Li Tim G. J. Rudner A. Wilson BDL 19 27 0 31 May 2023
Low-rank extended Kalman filtering for online learning of neural networks from streaming data Peter Chang Gerardo Duran-Martín Alexander Y. Shestopaloff Matt Jones Kevin P. Murphy BDL 38 17 0 31 May 2023
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution Vikrant Rangnekar Uddeshya Upadhyay Zeynep Akata Biplab Banerjee 19 4 0 27 May 2023
Improving Neural Additive Models with Bayesian Principles Kouroche Bouchiat Alexander Immer Hugo Yèche Gunnar Rätsch Vincent Fortuin BDL MedIm 19 6 0 26 May 2023
Uncertainty and Structure in Neural Ordinary Differential Equations Katharina Ott Michael Tiemann Philipp Hennig AI4CE 24 5 0 22 May 2023
Bayesian Numerical Integration with Neural Networks Katharina Ott Michael Tiemann Philipp Hennig F. Briol BDL 11 3 0 22 May 2023
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial V. Nemani Luca Biggio Xun Huan Zhen Hu Olga Fink Anh Tran Yan Wang Xiaoge Zhang Chao Hu AI4CE 18 75 0 07 May 2023
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization Agustinus Kristiadi Alexander Immer Runa Eschenhagen Vincent Fortuin BDL UQCV 13 8 0 17 Apr 2023
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry J. G. Wiese Lisa Wimmer Theodore Papamarkou Bernd Bischl Stephan Günnemann David Rügamer 14 11 0 06 Apr 2023
Incorporating Unlabelled Data into Bayesian Neural Networks Mrinank Sharma Tom Rainforth Yee Whye Teh Vincent Fortuin SSL UQCV BDL 32 9 0 04 Apr 2023
Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty Kilian Zepf Selma Wanna M. Miani Juston Moore J. Frellsen Søren Hauberg Aasa Feragen Frederik Warburg UQCV 11 4 0 23 Mar 2023
Curvature-Sensitive Predictive Coding with Approximate Laplace Monte Carlo Umais Zahid Qinghai Guo Karl J. Friston Z. Fountas 16 3 0 09 Mar 2023
Exploration via Epistemic Value Estimation Simon Schmitt John Shawe-Taylor Hado van Hasselt OffRL 11 1 0 07 Mar 2023
Variational Linearized Laplace Approximation for Bayesian Deep Learning Luis A. Ortega Simón Rodríguez Santana Daniel Hernández-Lobato BDL UQCV 28 4 0 24 Feb 2023
Likelihood Annealing: Fast Calibrated Uncertainty for Regression Uddeshya Upadhyay Jae Myung Kim Cordelia Schmidt Bernhard Schölkopf Zeynep Akata BDL UQCV 11 1 0 21 Feb 2023
Guided Deep Kernel Learning Idan Achituve Gal Chechik Ethan Fetaya BDL 31 5 0 19 Feb 2023
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization Agustinus Kristiadi Felix Dangel Philipp Hennig 11 10 0 14 Feb 2023
Fixing Overconfidence in Dynamic Neural Networks Lassi Meronen Martin Trapp Andrea Pilzer Le Yang Arno Solin BDL 21 16 0 13 Feb 2023
Fortuna: A Library for Uncertainty Quantification in Deep Learning Gianluca Detommaso Alberto Gasparin Michele Donini Matthias Seeger A. Wilson Cédric Archambeau UQCV BDL 23 14 0 08 Feb 2023
Variational Inference on the Final-Layer Output of Neural Networks Yadi Wei R. Khardon BDL UQCV 11 0 0 05 Feb 2023
Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval Frederik Warburg M. Miani Silas Brack Søren Hauberg UQCV BDL 11 7 0 02 Feb 2023
Posterior sampling with CNN-based, Plug-and-Play regularization with applications to Post-Stack Seismic Inversion M. Izzatullah T. Alkhalifah J. Romero M. Corrales N. Luiken M. Ravasi 29 2 0 30 Dec 2022
ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods Philipp Bachmann Daniel Gedon Fredrik K. Gustafsson Antônio H. Ribeiro E. Lampa S. Gustafsson Johan Sundström Thomas B. Schon 12 1 0 21 Dec 2022
Calibrating AI Models for Wireless Communications via Conformal Prediction K. Cohen Sangwoo Park Osvaldo Simeone S. Shamai 24 6 0 15 Dec 2022
The Implicit Delta Method Nathan Kallus James McInerney 12 1 0 11 Nov 2022
Do Bayesian Neural Networks Need To Be Fully Stochastic? Mrinank Sharma Sebastian Farquhar Eric T. Nalisnick Tom Rainforth BDL 16 52 0 11 Nov 2022