bayesian neural network

Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement AAAI 2026 py/cuTAGI: An Open-Source Library for Tractable Approximate Gaussian Inference in Bayesian Neural Networks JMLR 2026 The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks JMLR 2026 Bayesian Low-Rank Learning (Bella): A Practical Approach to Bayesian Neural Networks AAAI 2025 Bayesian Scalar-on-Image Regression with a Spatially Varying Single-layer Neural Network Prior JMLR 2025 Bayesian Treatment of the Spectrum of the Empirical Kernel in (Sub)Linear-Width Neural Networks ICLR 2025 Bridging the Gap between Variational Inference and Stochastic Gradient MCMC in Function Space ICLR 2025 Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification ICLR 2025 Deep Kernel Posterior Learning under Infinite Variance Prior Weights ICLR 2025 Do Bayesian Neural Networks Actually Behave Like Bayesian Models? ICML 2025 ELBOing Stein: Variational Bayes with Stein Mixture Inference ICLR 2025 ELBO, regularized maximum likelihood, and their common one-sample approximation for training stochastic neural networks UAI 2025 Flat Posterior Does Matter For Bayesian Model Averaging UAI 2025 From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation AISTATS 2025 Functional Stochastic Gradient MCMC for Bayesian Neural Networks AISTATS 2025 Function-Space MCMC for Bayesian Wide Neural Networks AISTATS 2025 GAUDA: Generative Adaptive Uncertainty-Guided Diffusion-Based Augmentation for Surgical Segmentation WACV 2025 Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles UAI 2025 Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks ICLR 2025 On Local Posterior Structure in Deep Ensembles AISTATS 2025 On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network Posteriors AISTATS 2025 Optimization Proxies using Limited Labeled Data and Training Time – A Semi-Supervised Bayesian Neural Network Approach ICML 2025 Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo ICLR 2025 Paths and Ambient Spaces in Neural Loss Landscapes AISTATS 2025 Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning AISTATS 2025