66 citations · 114 across the 17 of their papers we have counts for
5 papers · 1 filter
Do Bayesian Neural Networks Need To Be Fully Stochastic?
Mrinank Sharma, Sebastian Farquhar, Eric Nalisnick +1
We investigate the benefit of treating all the parameters in a Bayesian neural network stochastically and find compelling theoretical and empirical evidence that this standard cons…
Sampling-based inference for large linear models, with application to linearised Laplace
Javier Antorán, Shreyas Padhy, Riccardo Barbano +3
Large-scale linear models are ubiquitous throughout machine learning, with contemporary application as surrogate models for neural network uncertainty quantification; that is, the…
Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions
Urja Khurana, Ivar Vermeulen, Eric Nalisnick +2
\textbf{Offensive Content Warning}: This paper contains offensive language only for providing examples that clarify this research and do not reflect the authors' opinions. Please b…
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
Javier Antorán, David Janz, James Urquhart Allingham +4
The linearised Laplace method for estimating model uncertainty has received renewed attention in the Bayesian deep learning community. The method provides reliable error bars and a…
Adversarial Defense via Image Denoising with Chaotic Encryption
Shi Hu, Eric Nalisnick, Max Welling
In the literature on adversarial examples, white box and black box attacks have received the most attention. The adversary is assumed to have either full (white) or no (black) acce…