Bayesian Neural Networks

Also known as BNN

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Uncertainty Estimation and Generalization Bounds for Modern Deep Learning
Jun 11, 2026Luis A. OrtegaBayesian Neural Networks Neural Network Generalization
Department of Computer Science Machine Learning Group Madrid
Function-Space Priors for Bayesian Neural ODEs with Application to Vessel Trajectory Prediction
Jun 4, 2026Jaeyeong Lee, Wonmo Koo, Heeyoung KimBayesian Neural Networks Probabilistic Forecasting
aDepartment of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Bayesian Inference with Shaped Deep Non-linear MLPs
May 29, 2026Boris Hanin, Tianze JiangBayesian Neural Networks Multilayer Perceptrons
Princeton University
Active Continual Learning with Metaplastic Binary Bayesian Neural Networks
May 28, 2026Kellian Cottart, Théo Ballet, Djohan Bonnet +1Bayesian Neural Networks Binary Neural Networks
Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France · Forschungszentrum Jülich, Germany
Kernel Renormalization in Bayesian Deep Neural Networks: the Equivalent Wishart Ansatz in the Proportional Regime
May 28, 2026Paolo Baglioni, Christian Keup, Vincenzo Zimbardo +4Bayesian Neural Networks Neural Network Generalization
1INFN, Sezione di Milano Bicocca, Piazza della Scienza 3, 20126, Milano, Italy · 2INFN, Gruppo Collegato di Parma, Parco Area delle Scienze 7/A, 43124 Parma, Italy · 3Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Universit`a degli Studi di Parma, Parco Area delle Scienze, 7/A 43124 Parma, Italy +2
SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning
May 26, 2026Wenyuan Zhao, Rui Tuo, Chao TianBayesian Neural Networks Gaussian Processes
Department of Electrical and Computer Engineering, Texas A&M University, College Station, US · Department of Industrial and Systems Engineering, Texas A&M University, College Station, US
On the Epistemic Uncertainty of Overparametrized Neural Networks
May 24, 2026David RügamerBayesian Neural Networks Parameter Identifiability
Department of Statistics, LMU Munich, Munich, Germany · Munich Center for Machine Learning (MCML), Munich, Germany
DBPnet: Damper Characteristics-Based Bayesian Physics-Informed Neural Network for Wheel Load Estimation
May 24, 2026Tianyi Wang, Tianyi Zeng, Zimo Zeng +8Bayesian Neural Networks Physics-Informed ML
Department of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin, Austin, TX 78712, USA. · School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai 200240, China. · College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China. +3
Uncertainty Decomposition via Cyclical SG-MCMC and Soft-label Learning for Subjective NLP
May 23, 2026Keito Inoshita, Takato UenoSoft-Label Learning Bayesian Neural Networks
Faculty of Business and Commerce, Kansai University, Suita, Japan · Data Science and AI Innovation Research Promotion Center, Shiga University, Hikone, Japan · Graduate School of Data Science, Shiga University, Hikone, Japan
Uncertainty-aware classification and triage of structural heart disease using electrocardiography and echocardiography metrics
May 21, 2026Mitchel J. ColebankBayesian Neural Networks Uncertainty Quantification
Department of Mathematics, University of South Carolina,, Columbia, 29208, South Carolina, USA
Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning
May 20, 2026Emanuel Sommer, David RügamerBayesian Neural Networks Efficient Neural Network Inference
Department of Statistics, LMU Munich, Munich, Germany · Munich Center for Machine Learning, Munich, Germany.
A Unified Framework for Uncertainty-Aware Explainable Artificial Intelligence: A Case Study in Power Quality Disturbance Classification
May 20, 2026Yinsong Chen, Samson S. Yu, Zhong Li +1Bayesian Neural Networks Feature Attribution
School of Engineering, Deakin University, Melbourne, 3216, VIC, Australia · Faculty of Mathematics and Computer Science, FernUniversität in Hagen, 58084, Germany · Department of Computing Technologies, Swinburne University of Technology, Hawthorn, 3122, VIC, Australia
Federated Martingale Posterior Samping
May 18, 2026Boning Zhang, Matteo Zecchin, Mingzhao Guo +2Bayesian Neural Networks Posterior Sampling
School of Computing, University of Glasgow, Glasgow G12 8QQ, U.K. · Communication Systems Department, EURECOM, 06904 Sophia Antipolis, France · Institute for Intelligent Networked Systems, Northeastern University London, London E1 8PH, U.K.
Don't Stop Me Yet: Sampling Loss Minima via Dissipative Riemannian Mechanics
May 14, 2026Albert Kjøller Jacobsen, Leo Uhre Jakobsen, Johanna Marie Gegenfurtner +1Bayesian Neural Networks Dynamical Systems
Section for Cognitive Systems, Technical University of Denmark · Center for Quantum Information Physics, New York University
Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification
May 12, 2026Julian Rodemann, Alexander Marquard, Thomas Augustin +1Bayesian Neural Networks Uncertainty Quantification
Rational Intelligence Lab, CISPA Helmholtz Center for Information Security · Department of Statistics, LMU Munich · Department of Computer Science, The University of Manchester
Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces
May 12, 2026Jeunghun Oh, Kyeongwon Lee, Jaeyong Lee +1Bayesian Neural Networks Kolmogorov-Arnold Networks
Department of Statistics, Seoul National University · Department of Statistics, Inha University · Department of Mathematics, University of Maryland
Native Explainability for Bayesian Confidence Propagation Neural Networks: A Framework for Trusted Brain-Like AI
May 12, 2026Georgios Makridis, Georgios Fatouros, John Soldatos +2Bayesian Neural Networks Explainable Artificial Intelligence
ExpertAI-Lux S.a r.l · University of Piraeus, Greece, Department of Digital Systems · University of Glasgow School of Computing Science
Optimality of Sub-network Laplace Approximations: New Results and Methods
May 9, 2026Swarnali Raha, Kshitij Khare, Rohit K PatraBayesian Neural Networks Uncertainty Quantification
Department of Statistics University of Florida Gainesville, FL · Linkedin Inc. New York, NY
Direct Bethe Free Energy Minimization for Bayesian Neural Networks
May 8, 2026Pavel ProchazkaBayesian Neural Networks Variational Inference
Cisco Inc.
Bayesian Fine-tuning in Projected Subspaces
May 8, 2026Viktar Dubovik, Patryk Marszałek, Jacek Tabor +1Bayesian Neural Networks Fine-Tuning
Jagiellonian University, Kraków, Poland · Doctoral School of Exact and Natural Sciences, Jagiellonian University, Kraków, Poland
Architecture-agnostic Lipschitz-constant Bayesian header and its application to resolve semantically proximal classification errors with vision transformers
May 7, 2026Frederik Schäfer, Luis Mandl, Lars Kälber +1Bayesian Neural Networks Vision Transformer
Institute of Structural Mechanics and Dynamics in Aerospace Engineering, University of Stuttgart · Rockwell Collins Deutschland GmbH · Department of Hepatobiliary Surgery and Visceral Transplantation, University of Leipzig Medical Center
Uncertainty Estimation in Instance Segmentation of Affordances via Bayesian Visual Transformers
May 5, 2026Lorenzo Mur-Labadia, Ruben Martinez-Cantina, Jose J. GuerreroBayesian Neural Networks Instance Segmentation
aI3A, Universidad de Zaragoza, Zaragoza, 50018, Spain
Neural Aided Kalman Filtering for UAV State Estimation in Degraded Sensing Environments
Apr 30, 2026Akhil Gupta, Erhan GuvenBayesian Neural Networks Kalman Filtering
Whiting School of Engineering EP Program, Johns Hopkins University, Baltimore, Maryland, USA
Learning with Embedded Linear Equality Constraints via Variational Bayesian Inference
Apr 27, 2026Matthew Marsh, Benoît Chachuat, Antonio del Rio ChanonaBayesian Neural Networks Uncertainty Quantification
Sargent Centre for Process Systems Engineering, Imperial College London
BaLoRA: Bayesian Low-Rank Adaptation of Large Scale Models
Apr 27, 2026Dario Coscia, Sindy Löwe, Max WellingBayesian Neural Networks Uncertainty Quantification
mathLab, SISSA · CuspAI
A Tale of Two Variances: When Single-Seed Benchmarks Fail in Bayesian Deep Learning
Apr 25, 2026Qishi Zhan, Minxuan Hu, Liang He +2Bayesian Neural Networks Deep Ensembles
Department of Mathematical and Statistical Sciences Marquette University, USA · Cornell Ann S. Bowers College of Computing and Information Science Cornell University, USA · Physics Science and Engineering Tongji University, China +2
Unstable Rankings in Bayesian Deep Learning Evaluation
Apr 25, 2026Qishi Zhan, Minxuan Hu, Guansu Wang +2Bayesian Neural Networks Statistical Power Analysis
Department of Mathematical and Statistical Sciences Marquette University, USA · Cornell University, USA · The University of Melbourne, Australia +2
Algorithm and Hardware Co-Design for Efficient Complex-Valued Uncertainty Estimation
Apr 21, 2026Zehuan Zhang, Mark Chen, He Li +1Bayesian Neural Networks Uncertainty Quantification
Department of Computing, Imperial College London · 2Southeast University
Practical Bayesian Inference for Speech SNNs: Uncertainty and Loss-Landscape Smoothing
Apr 9, 2026Yesmine Abdennadher, Philip N. GarnerSpiking Neural Networks Bayesian Neural Networks
Idiap Research Institute, Martigny, Switzerland
Sampling Parallelism for Fast and Efficient Bayesian Learning
Apr 6, 2026Asena Karolin Özdemir, Lars H. Heyen, Arvid Weyrauch +3Bayesian Neural Networks Memory-Efficient Training
Karlsruhe Institute of Technology Karlsruhe, Germany · Helmholtz AI

Jun 11, 2026J/K move · Enter open · S save

Uncertainty Estimation and Generalization Bounds for Modern Deep Learning

Luis A. Ortega

Department of Computer Science Machine Learning Group Madrid

Bayesian Neural Networks Neural Network Generalization PAC-Bayesian Generalization Bounds Uncertainty Quantification Variational Inference Uncertainty Calibration

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This thesis investigates how Bayesian principles can deepen our understanding of modern deep learning systems. While neural networks achieve remarkable predictive performance, their ability to generalize and to quantify uncertainty remains only partly understood. This thesis approaches this challenge from both methodological and theoretical angles: unifying Bayesian inference, function-space modeling, and large-deviation theory under a common probabilistic perspective. On the methodological side, the thesis introduces the Deep Variational Implicit Process (DVIP), a scalable Bayesian framework that extends implicit processes to deep architectures. Complementing this, two post-hoc methods -- the Variational Linearized Laplace Approximation (VaLLA) and the Fixed-Mean Gaussian Process (FMGP) -- are proposed to equip pretrained deterministic networks with calibrated uncertainty estimates. The theoretical contributions focus on one of the central open questions in modern machine learning: why do large, over-parameterized neural networks generalize so well? To address this, the thesis develops a unified probabilistic framework that connects three key mechanisms -- diversity, smoothness, and stochasticity -- within the language of PAC-Bayesian and large-deviation theory.