11 citations · 33 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
Deep Classifiers with Label Noise Modeling and Distance Awareness
Vincent Fortuin, Mark Collier, Florian Wenzel +7
Uncertainty estimation in deep learning has recently emerged as a crucial area of interest to advance reliability and robustness in safety-critical applications. While there have b…
Depth Uncertainty in Neural Networks
Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
Existing methods for estimating uncertainty in deep learning tend to require multiple forward passes, making them unsuitable for applications where computational resources are limi…
Variational Depth Search in ResNets
Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
One-shot neural architecture search allows joint learning of weights and network architecture, reducing computational cost. We limit our search space to the depth of residual netwo…