283 citations · 1k across the 14 of their papers we have counts for
14 papers
Quantifying Uncertainty for Machine Learning Based Diagnostic
Owen Convery, Lewis Smith, Yarin Gal +1
Virtual Diagnostic (VD) is a deep learning tool that can be used to predict a diagnostic output. VDs are especially useful in systems where measuring the output is invasive, limite…
Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective
Lewis Smith, Joost van Amersfoort, Haiwen Huang +2
ResNets constrained to be bi-Lipschitz, that is, approximately distance preserving, have been a crucial component of recently proposed techniques for deterministic uncertainty quan…
Uncertainty Quantification for Virtual Diagnostic of Particle Accelerators
Owen Convery, Lewis Smith, Yarin Gal +1
Virtual Diagnostic (VD) is a computational tool based on deep learning that can be used to predict a diagnostic output. VDs are especially useful in systems where measuring the out…
On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty
Joost van Amersfoort, Lewis Smith, Andrew Jesson +2
Inducing point Gaussian process approximations are often considered a gold standard in uncertainty estimation since they retain many of the properties of the exact GP and scale to…
Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
Mike Walmsley, Chris Lintott, Tobias Geron +15
We present Galaxy Zoo DECaLS: detailed visual morphological classifications for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint. Deeper DECaLS ima…
Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders
Mizu Nishikawa-Toomey, Lewis Smith, Yarin Gal
The growth in the number of galaxy images is much faster than the speed at which these galaxies can be labelled by humans. However, by leveraging the information present in the eve…