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…
Capsule Networks -- A Probabilistic Perspective
Lewis Smith, Lisa Schut, Yarin Gal +1
'Capsule' models try to explicitly represent the poses of objects, enforcing a linear relationship between an object's pose and that of its constituent parts. This modelling assump…
Uncertainty Estimation Using a Single Deep Deterministic Neural Network
Joost van Amersfoort, Lewis Smith, Yee Whye Teh +1
We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. Our approach, determin…
Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations
Sebastian Farquhar, Lewis Smith, Yarin Gal
We challenge the longstanding assumption that the mean-field approximation for variational inference in Bayesian neural networks is severely restrictive, and show this is not the c…
A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks
Angelos Filos, Sebastian Farquhar, Aidan N. Gomez +6
Evaluation of Bayesian deep learning (BDL) methods is challenging. We often seek to evaluate the methods' robustness and scalability, assessing whether new tools give `better' unce…
Flood Detection On Low Cost Orbital Hardware
Gonzalo Mateo-Garcia, Silviu Oprea, Lewis Smith +5
Satellite imaging is a critical technology for monitoring and responding to natural disasters such as flooding. Despite the capabilities of modern satellites, there is still much t…
Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
Mike Walmsley, Lewis Smith, Chris Lintott +10
We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN…
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…
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…