2.9k citations · 12k across the 144 of their papers we have counts for
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Vprop: Variational Inference using RMSprop
Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt +1
Many computationally-efficient methods for Bayesian deep learning rely on continuous optimization algorithms, but the implementation of these methods requires significant changes t…
Real Time Image Saliency for Black Box Classifiers
Piotr Dabkowski, Yarin Gal
In this work we develop a fast saliency detection method that can be applied to any differentiable image classifier. We train a masking model to manipulate the scores of the classi…
Concrete Dropout
Yarin Gal, Jiri Hron, Alex Kendall
Dropout is used as a practical tool to obtain uncertainty estimates in large vision models and reinforcement learning (RL) tasks. But to obtain well-calibrated uncertainty estimate…
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Alex Kendall, Yarin Gal, Roberto Cipolla
Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives. In this paper we make the observation that the performa…
Dropout Inference in Bayesian Neural Networks with Alpha-divergences
Yingzhen Li, Yarin Gal
To obtain uncertainty estimates with real-world Bayesian deep learning models, practical inference approximations are needed. Dropout variational inference (VI) for example has bee…
Deep Bayesian Active Learning with Image Data
Yarin Gal, Riashat Islam, Zoubin Ghahramani
Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an…
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Alex Kendall, Yarin Gal
There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for unc…