research
∙
07/07/2022
Challenges and Pitfalls of Bayesian Unlearning
Machine unlearning refers to the task of removing a subset of training d...
share
research
∙
08/22/2021
Efficient Gaussian Neural Processes for Regression
Conditional Neural Processes (CNP; Garnelo et al., 2018) are an attracti...
research
∙
02/12/2021
Bayesian Neural Network Priors Revisited
Isotropic Gaussian priors are the de facto standard for modern Bayesian ...
research
∙
06/21/2020
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
Deep generative models often perform poorly in real-world applications d...
research
∙
08/13/2019
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
In this paper we introduce the ice-start problem, i.e., the challenge of...
research
∙
01/18/2018
Overpruning in Variational Bayesian Neural Networks
The motivations for using variational inference (VI) in neural networks ...
research
∙
07/10/2016
Magnetic Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct...
research
∙
03/09/2015