2.9k citations · 12k across the 144 of their papers we have counts for
9 papers · 1 filter
On the Importance of Strong Baselines in Bayesian Deep Learning
Jishnu Mukhoti, Pontus Stenetorp, Yarin Gal
Like all sub-fields of machine learning Bayesian Deep Learning is driven by empirical validation of its theoretical proposals. Given the many aspects of an experiment it is always…
Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control
Rhiannon Michelmore, Marta Kwiatkowska, Yarin Gal
A rise in popularity of Deep Neural Networks (DNNs), attributed to more powerful GPUs and widely available datasets, has seen them being increasingly used within safety-critical do…
Evaluating Bayesian Deep Learning Methods for Semantic Segmentation
Jishnu Mukhoti, Yarin Gal
Deep learning has been revolutionary for computer vision and semantic segmentation in particular, with Bayesian Deep Learning (BDL) used to obtain uncertainty maps from deep models…
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt +3
Uncertainty computation in deep learning is essential to design robust and reliable systems. Variational inference (VI) is a promising approach for such computation, but requires m…
Sufficient Conditions for Idealised Models to Have No Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks
Yarin Gal, Lewis Smith
We prove, under two sufficient conditions, that idealised models can have no adversarial examples. We discuss which idealised models satisfy our conditions, and show that idealised…
Loss-Calibrated Approximate Inference in Bayesian Neural Networks
Adam D. Cobb, Stephen J. Roberts, Yarin Gal
Current approaches in approximate inference for Bayesian neural networks minimise the Kullback-Leibler divergence to approximate the true posterior over the weights. However, this…
Towards Robust Evaluations of Continual Learning
Sebastian Farquhar, Yarin Gal
Experiments used in current continual learning research do not faithfully assess fundamental challenges of learning continually. Instead of assessing performance on challenging and…
Understanding Measures of Uncertainty for Adversarial Example Detection
Lewis Smith, Yarin Gal
Measuring uncertainty is a promising technique for detecting adversarial examples, crafted inputs on which the model predicts an incorrect class with high confidence. But many meas…
BRUNO: A Deep Recurrent Model for Exchangeable Data
Iryna Korshunova, Jonas Degrave, Ferenc Huszár +3
We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations. Our model is provab…