3 citations · 3 across the 2 of their papers we have counts for
4 papers · 1 filter
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling
Andreas Kirsch, Jannik Kossen, Yarin Gal
Principled Bayesian deep learning (BDL) does not live up to its potential when we only focus on marginal predictive distributions (marginal predictives). Recent works have highligh…
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning
Andreas Kirsch, Clare Lyle, Yarin Gal
The Information Bottleneck principle offers both a mechanism to explain how deep neural networks train and generalize, as well as a regularized objective with which to train models…
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
Andreas Kirsch, Joost van Amersfoort, Yarin Gal
We develop BatchBALD, a tractable approximation to the mutual information between a batch of points and model parameters, which we use as an acquisition function to select multiple…
MDP environments for the OpenAI Gym
Andreas Kirsch
The OpenAI Gym provides researchers and enthusiasts with simple to use environments for reinforcement learning. Even the simplest environment have a level of complexity that can ob…