Bayesian Neural Networks

Also known as BNN

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6 papers in the last four weeks, up 20% on the four weeks before. 0.0% of all new papers.

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Variational Filtering for Bayesian Last Layers with an Independent Binary Probit Surrogate
Oct 12, 2026Dimitrije Markovic, Conor Heins, Alexander Tschantz +1Bayesian Neural Networks Bayesian Inference
Independent Researcher Vienna, Austria · Independent Researcher Berlin, Germany · University of Sussex Brighton, UK +1
When Has a Bayesian Neural Network Sampled Enough? Adaptive Inference Time with Statistical Guarantees
Oct 8, 2026Fabian Denoodt, Sibylle HessAdaptive Sampling Bayesian Neural Networks
Eindhoven University of Technology
Sparsifying Stochasticity, Not Capacity: Partial Stochasticity via Deep Weight Factorization of Prior Scales
Oct 7, 2026Marius P Linhard, Maurizio FilipponeBayesian Neural Networks Conditional Distribution Estimation
Université Grenoble Alpes, France · KAUST, Saudi Arabia
The Impact of Likelihood Tempering on the Limiting Predictive Moments of Variational Bayesian Linear Neural Networks
Oct 6, 2026Ian Zhang, Thibault RandrianarisoaBayesian Neural Networks Variational Inference
Department of Statistical Sciences, University of Toronto · Dunlap Institute for Astronomy and Astrophysics
Full-Covariance Smoothing of Bayesian Neural Networks for Online Adaptation
Sep 23, 2026Oren Wright, Haoming Jing, Qiaoan Shen +3Bayesian Neural Networks Bayesian Filtering
Department of Electrical and Computer Engineering, Carnegie Mellon University · Software Engineering Institute, Carnegie Mellon University · Fujitsu Research of America
Bayesian classification of astronomical spectra with class uncertainties
Sep 18, 2026Simon Barton, Martin Sahlén, Andreas Korn +1Multiclass Classification Uncertainty Calibration
Theoretical Astrophysics, Division of Astronomy and Space Physics, Department of Physics and Astronomy, Uppsala University, Box 524, 751 20 Uppsala, Sweden. · Observational Astrophysics, Division of Astronomy and Space Physics, Department of Physics and Astronomy, Uppsala University, Box 524, 751 20 Uppsala, Sweden · Division of High Energy Physics, Department of Physics and Astronomy, Uppsala University, Box 524, 751 20 Uppsala, Sweden +1
Certified Uncertainty Propagation in One-Shot Federated Bayesian Models via Posterior Event Transport
Sep 14, 2026Mahyar Mohammadi, Mohammad Hossein Badiei, Abolfazl Yaghmaei +1Bayesian Neural Networks Neural Network Verification
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 1417614411, Iran · School of Computer Science, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5746, Tehran, Iran
Online Bayesian Node Classification on Inductive Graphs under Distribution Shift
Sep 12, 2026Jinwen Xu, Gonzalo Mateos Buckstein, Qin LuNode Classification Graph Neural Networks
School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA · Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling
Sep 11, 2026Deifilia Kieckhefen, Juan Pedro Gutiérrez Hermosillo Muriedas, Lars Helge Heyen +12Bayesian Neural Networks Uncertainty Quantification
Karlsruhe Institute of Technology, Germany · Forschungszentrum Jülich, Germany · CSC – IT Center for Science Ltd., Finnland +1
HiBRIDGE: A Hierarchical Bayesian Neural Network Framework for Interpretable Dialogue Management in Group-Robot Interaction
Sep 8, 2026Massimiliano Nigro, Hatice Gunes, Micol Spitale +1Bayesian Neural Networks Human-Robot Interaction
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy · Affective Intelligence and Robotics (AFAR) Lab, Department of Computer Science and Technology, University of Cambridge, UK
More accurate behavioral predictions with hybrid Bayesian-connectionist models
Aug 23, 2026Brenden M. Lake, Akshay K. Jagadish, Guangyuan JiangComputational Cognitive Modeling Bayesian Neural Networks
Departments of Computer Science and Psychology, Princeton University · Princeton AI Lab, Princeton University · Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
Hidden Axis of Uncertainty: Latent-Posterior Alignment in Graph Neural Networks with Bayesian Output Layers
Aug 21, 2026Suk Hoon Choi, Damdae Park, Junhyuk Choi +4Bayesian Neural Networks Graph Neural Networks
Clean Energy Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea. · Department of Chemical and Biological Engineering, Korea University, Seoul 02841, Republic of Korea. · Division of Energy & Environment Technology, Korea University of Science and Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea.
Comment on "Modeling rapid language learning by distilling Bayesian priors into artificial neural networks"
Aug 13, 2026Orr Well, Idan Tarshish, Nur Lan +1Bayesian Neural Networks Meta-Learning
1Tel Aviv University · 2École Normale Supérieure
Uncertainty-Aware Deep Learning for Genomics Applications: Insights from an Empirical Study
Aug 11, 2026Sepideh Saran, Mahsa Ghanbari, Uwe OhlerBayesian Neural Networks Uncertainty Quantification
The Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Germany · Department of Electrical Engineering and Computer Science, Technical University of Berlin, Germany · Department of Biology, Humboldt University of Berlin, Germany +1
Constrained Co-Design for Photonic Bayesian Neural Networks
Aug 3, 2026Hendrik Borras, Xiao Wang, Bernhard Klein +4Bayesian Neural Networks Uncertainty Quantification
Hardware and Artificial Intelligence Lab, Institute of Computer Engineering, Heidelberg University, Germany · Neuromorphic Quantumphotonics Kirchhoff-Institute for Physics, Heidelberg University, Germany
Uncertainty quantification for trustworthy deep learning: Methods and measures
Jul 30, 2026H. Martin Gillis, Thomas TrappenbergBayesian Neural Networks Uncertainty Quantification
Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance
Jul 28, 2026Pei-Hsuan Hsia, Lars H. Heyen, Arvid Weyrauch +4Bayesian Neural Networks Bayesian Inference
Scientific Computing Center, Karlsruhe Institute of Technology · Scientific Computing Center, Karlsruhe Institute of Technology; and Helmholtz AI
Guiding Posterior Exploration with Optimizer-Derived Geometry
Jul 28, 2026Moritz Schlager, Emanuel Sommer, Thomas Möllenhoff +1Bayesian Neural Networks Markov Chain Monte Carlo
TUM, MCML · LMU Munich, MCML · RIKEN AIP
DP-IVON-Gradsq: Differentially Private Squared-Gradient Improved Variational Online Newton
Jul 26, 2026Nour Jamoussi, Ikram Dridi, Giuseppe Serra +1Bayesian Neural Networks Privacy-Preserving ML
EURECOM, Sophia Antipolis, France · Concordia University, Montreal, Canada · University of Granada, Granada, Spain
Neural Feature Governance: Extending Atom Prevalence
Jul 23, 2026Idris Karel Seunda Ekwe, Patrick Tenga Shako, Ernest Parfait FokouéBayesian Neural Networks Uncertainty Quantification
African Institute for Mathematical Sciences (AIMS), Cameroon · Rochester Institute of Technology (RIT), US
Disentangling Model and Human Data Uncertainty in Apparent Facial Age Estimation
Jul 17, 2026Andrei Foitos, Ivo Pascal de Jong, Matias Valdenegro-ToroBayesian Neural Networks Uncertainty Quantification
Bernoulli Institute, University of Groningen, Groningen, NL
Width-Robust Learnability in Mean-Field Bayesian Neural Networks
Jul 7, 2026Dmitry Vaintrob, Kaarel HänniBayesian Neural Networks Neural Network Generalization
Principles of Intelligence
Uncertainty-Aware Last-Layer Adaptation of RETFound for Referable Diabetic Retinopathy Screening Under Dataset Shift
Jun 30, 2026Karim MardhaniBayesian Neural Networks Distribution Shift Robustness
University of Colorado Boulder Boulder, CO, USA
BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning
Jun 28, 2026Zhibin Duan, Yuhong Wang, Jiahong Fu +3Bayesian Neural Networks Low-Rank Adaptation
National Key Lab of Radar Signal Processing, Xidian University, Xi’an, Shaanxi 710071, China · School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, China
Equivariance and Augmentation for Bayesian Neural Networks
Jun 24, 2026Miaowen Dong, Axel Flinth, Jan E. GerkenBayesian Neural Networks Data Augmentation
Department of Mathematical Sciences, Chalmers University of Technology and the University of Gothenburg, SE-412 96 Gothenburg, Sweden. · Department of Mathematics and Mathematical Statistics, Umeå University, Linnéus väg 49, 901 87 Umeå, Sweden.
SOAP-Bubbles: Structured Weight Uncertainty for Neural Networks
Jun 22, 2026Adrian Robert Minut, Nico Daheim, Marco Miani +3Bayesian Neural Networks Neural Posterior Estimation
Sapienza University of Rome, Rome, Italy · Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science, Technical University of Darmstadt · Technical University of Denmark, Lyngby, Denmark +3
Embedding Linear Equality Constraints in Probabilistic Neural Networks for Dynamic Modelling
Jun 19, 2026Matthew Marsh, Benoit Chachuat, Antonio del Rio ChanonaNeural Surrogate Modeling Bayesian Neural Networks
The Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, UK
Calibrated Sampling-Free Uncertainty Estimation in Bayesian Deep Learning
Jun 15, 2026Tobias Jan Wieczorek, Leon de Andrade, Thomas Möllenhoff +1Bayesian Neural Networks Uncertainty Quantification
TU Darmstadt & hessian.AI, Darmstadt, Germany · RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
Bayesian 3D Steerable CNNs: Enabling Equivariance and Uncertainty Quantification Simultaneously
Jun 13, 2026Abhishek Keripale, Ponkrshnan Thiagarajan, Susanta GhoshBayesian Neural Networks Uncertainty Quantification
Department of Mechanical and Aerospace Engineering, Michigan Technological University, Houghton, MI 49931 · Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD 21218 · The Center for Artificial Intelligence at the Institute of Computing and Cybersystems, Michigan Technological University, USA
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

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Variational Filtering for Bayesian Last Layers with an Independent Binary Probit Surrogate

Dimitrije Markovic, Conor Heins, Alexander Tschantz, Christopher L. Buckley

Independent Researcher Vienna, Austria · Independent Researcher Berlin, Germany · University of Sussex Brighton, UK+1

Bayesian Neural Networks Bayesian Inference Bayesian Filtering Vision Foundation Model Adaptation

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Modern deep classifiers trained by gradient descent need many passes over the data to become accurate, and even then are not always well calibrated; post-hoc temperature scaling corrects calibration after training but cannot influence training-time accuracy or convergence. We propose a variational filtering algorithm for the last layer of deep classifiers, whose parameters are treated as the latent state of a linear-Gaussian state-space model. Each mini-batch is assimilated by an exact prediction step followed by a closed-form variational update, made conjugate by an independent binary probit (IBProbit) surrogate for the categorical likelihood. The filter's process noise stabilises the updates under feature drift and keeps the predictive distribution from collapsing to a point estimate, allowing the same procedure to be used for linear probing on a frozen backbone and for full-network fine-tuning. We evaluate on DINOv3 backbones against two baselines tuned under an identical protocol: AdamW with cross-entropy and post-hoc temperature scaling, and Variational Bayesian Last Layers (VBLL). As a linear probe on frozen features, IBProbit attains the best accuracy and negative log-likelihood on all five downstream tasks at the largest backbone, and on two of the five at the smallest. In out-of-distribution detection with CIFAR probes on the largest backbone, IBProbit reduces the false-positive rate at 95% true-positive rate by a factor of 1.4 to 3.2 relative to the better of the two baselines, on all three dataset pairs. When the whole network is fine-tuned on ImageNet-1k, IBProbit reaches the final performance of the cross-entropy baseline in half the wall-clock time.