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Lewis Smith
Lewis Smith
Verified email at kellogg.ox.ac.uk - Homepage
Title
Cited by
Cited by
Year
Understanding measures of uncertainty for adversarial example detection
L Smith, Y Gal
arXiv preprint arXiv:1803.08533, 2018
2522018
Uncertainty estimation using a single deep deterministic neural network
J Van Amersfoort, L Smith, YW Teh, Y Gal
International conference on machine learning, 9690-9700, 2020
1982020
Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning
M Walmsley, L Smith, C Lintott, Y Gal, S Bamford, H Dickinson, L Fortson, ...
Monthly Notices of the Royal Astronomical Society 491 (2), 1554-1574, 2020
952020
A systematic comparison of bayesian deep learning robustness in diabetic retinopathy tasks
A Filos, S Farquhar, AN Gomez, TGJ Rudner, Z Kenton, L Smith, ...
arXiv preprint arXiv:1912.10481, 2019
662019
Amphiphilic π-Allyliridium C,O-Benzoates Enable Regio- and Enantioselective Amination of Branched Allylic Acetates Bearing Linear Alkyl Groups
AT Meza, T Wurm, L Smith, SW Kim, JR Zbieg, CE Stivala, MJ Krische
Journal of the American Chemical Society 140 (4), 1275-1279, 2018
432018
Galaxy Zoo DECaLS: Detailed visual morphology measurements from volunteers and deep learning for 314 000 galaxies
M Walmsley, C Lintott, T Géron, S Kruk, C Krawczyk, KW Willett, ...
Monthly Notices of the Royal Astronomical Society 509 (3), 3966-3988, 2022
392022
Towards global flood mapping onboard low cost satellites with machine learning
G Mateo-Garcia, J Veitch-Michaelis, L Smith, SV Oprea, G Schumann, ...
Scientific reports 11 (1), 1-12, 2021
342021
Simple and scalable epistemic uncertainty estimation using a single deep deterministic neural network
J van Amersfoort, L Smith, YW Teh, Y Gal
322020
Sufficient conditions for idealised models to have no adversarial examples: a theoretical and empirical study with bayesian neural networks
Y Gal, L Smith
arXiv preprint arXiv:1806.00667, 2018
322018
On feature collapse and deep kernel learning for single forward pass uncertainty
J van Amersfoort, L Smith, A Jesson, O Key, Y Gal
arXiv preprint arXiv:2102.11409, 2021
302021
Improving deterministic uncertainty estimation in deep learning for classification and regression
J van Amersfoort, L Smith, A Jesson, O Key, Y Gal
292021
Liberty or depth: Deep bayesian neural nets do not need complex weight posterior approximations
S Farquhar, L Smith, Y Gal
Advances in Neural Information Processing Systems 33, 4346-4357, 2020
282020
Benchmarking Bayesian deep learning with diabetic retinopathy diagnosis
A Filos, S Farquhar, AN Gomez, TGJ Rudner, Z Kenton, L Smith, ...
Preprint at https://arxiv. org/abs/1912.10481, 2019
172019
Uncertainty quantification for virtual diagnostic of particle accelerators
O Convery, L Smith, Y Gal, A Hanuka
Physical Review Accelerators and Beams 24 (7), 074602, 2021
102021
Idealised bayesian neural networks cannot have adversarial examples: Theoretical and empirical study
Y Gal, L Smith
arXiv preprint arXiv:1806.00667, 2018
72018
Try depth instead of weight correlations: Mean-field is a less restrictive assumption for deeper networks
S Farquhar, L Smith, Y Gal
52020
Flood detection on low cost orbital hardware
G Mateo-Garcia, S Oprea, L Smith, J Veitch-Michaelis, G Schumann, ...
arXiv preprint arXiv:1910.03019, 2019
52019
Capsule Networks--A Probabilistic Perspective
L Smith, L Schut, Y Gal, M van der Wilk
arXiv preprint arXiv:2004.03553, 2020
42020
Try depth instead of weight correlations: Mean field is a less restrictive assumption for variational inference in deep networks
S Farquhar, L Smith, Y Gal
Bayesian Deep Learning Workshop at NeurIPS, 2020
42020
Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective
L Smith, J van Amersfoort, H Huang, S Roberts, Y Gal
arXiv preprint arXiv:2106.02469, 2021
32021
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Articles 1–20