63 citations · 203 across the 11 of their papers we have counts for
14 papers
Position: agentic AI orchestration should be Bayes-consistent
Theodore Papamarkou, Pierre Alquier, Matthias Bauer +27
LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to co…
Spatial Functa: Scaling Functa to ImageNet Classification and Generation
Matthias Bauer, Emilien Dupont, Andy Brock +3
Neural fields, also known as implicit neural representations, have emerged as a powerful means to represent complex signals of various modalities. Based on this Dupont et al. (2022…
Regularising for invariance to data augmentation improves supervised learning
Aleksander Botev, Matthias Bauer, Soham De
Data augmentation is used in machine learning to make the classifier invariant to label-preserving transformations. Usually this invariance is only encouraged implicitly by includi…
Laplace Redux -- Effortless Bayesian Deep Learning
Erik Daxberger, Agustinus Kristiadi, Alexander Immer +3
Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty qu…
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
Alexander Immer, Matthias Bauer, Vincent Fortuin +2
Marginal-likelihood based model-selection, even though promising, is rarely used in deep learning due to estimation difficulties. Instead, most approaches rely on validation data,…
Generalized Doubly Reparameterized Gradient Estimators
Matthias Bauer, Andriy Mnih
Efficient low-variance gradient estimation enabled by the reparameterization trick (RT) has been essential to the success of variational autoencoders. Doubly-reparameterized gradie…