11 citations · 33 across the 10 of their papers we have counts for
3 papers · 1 filter
Bayesian Deep Learning via Subnetwork Inference
Erik Daxberger, Eric Nalisnick, James Urquhart Allingham +2
The Bayesian paradigm has the potential to solve core issues of deep neural networks such as poor calibration and data inefficiency. Alas, scaling Bayesian inference to large weigh…
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…