On the Challenges and Opportunities in Generative AI
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood
A primer on Bayesian neural networks: review and debates
Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072)
Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo
Incorporating Unlabelled Data into Bayesian Neural Networks
Understanding pathologies of deep heteroskedastic regression
Deep classifiers with label noise modeling and distance awareness