"bayesian neural networks" Papers
11 papers found
Bridging the Gap between Variational Inference and Stochastic Gradient MCMC in Function Space
Mengjing Wu, Junyu Xuan, Jie Lu
ICLR 2025poster
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification
Kaizheng Wang, Fabio Cuzzolin, Keivan Shariatmadar et al.
ICLR 2025posterarXiv:2405.15047
3
citations
Deep Kernel Posterior Learning under Infinite Variance Prior Weights
Jorge Loría, Anindya Bhadra
ICLR 2025posterarXiv:2410.01284
Variational Bayesian Pseudo-Coreset
Hyungi Lee, Seungyoo Lee, Juho Lee
ICLR 2025posterarXiv:2502.21143
VIKING: Deep variational inference with stochastic projections
Samuel Matthiesen, Hrittik Roy, Nicholas Krämer et al.
NeurIPS 2025posterarXiv:2510.23684
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta et al.
ICML 2024poster
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou et al.
ICML 2024poster
Improving Neural Additive Models with Bayesian Principles
Kouroche Bouchiat, Alexander Immer, Hugo Yèche et al.
ICML 2024poster
Learning to Explore for Stochastic Gradient MCMC
SeungHyun Kim, Seohyeon Jung, SeongHyeon Kim et al.
ICML 2024poster
Partially Stochastic Infinitely Deep Bayesian Neural Networks
Sergio Calvo Ordoñez, Matthieu Meunier, Francesco Piatti et al.
ICML 2024poster
Reparameterized Importance Sampling for Robust Variational Bayesian Neural Networks
Yunfei Long, Zilin Tian, Liguo Zhang et al.
ICML 2024poster