paper-conference

Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning

Matthew Ashman, Tommy Rochussen, Adrian Weller

Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo

Szilvia Ujváry, Gergely Flamich, Vincent Fortuin, José Miguel Hernández Lobato

Learning with noisy labels by adaptive gradient-based outlier removal

Anastasiia Sedova, Lena Zellinger, Benjamin Roth

Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization

Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin

Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks

Alexander Möllers, Alexander Immer, Elvin Isufi, Vincent Fortuin

From Hyperbolic Geometry Back to Word Embeddings

Zhenisbek Assylbekov, Sultan Nurmukhamedov, Arsen Sheverdin, Thomas Mach

Bayesian neural network priors revisited

Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W Ober, Florian Wenzel, Gunnar Rätsch, Richard E Turner, Mark van der Wilk, Laurence Aitchison

Data augmentation in Bayesian neural networks and the cold posterior effect

Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison

Meta-learning richer priors for VAEs

Marcello Massimo Negri, Vincent Fortuin, Jan Stuehmer

Neural Variational Gradient Descent

Lauro Langosco di Langosco, Vincent Fortuin, Heiko Strathmann