no code implementations • ICML 2020 • Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal
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.
no code implementations • 24 Apr 2024 • Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Tom Lieberum, Vikrant Varma, János Kramár, Rohin Shah, Neel Nanda
Recent work has found that sparse autoencoders (SAEs) are an effective technique for unsupervised discovery of interpretable features in language models' (LMs) activations, by finding sparse, linear reconstructions of LM activations.
no code implementations • 29 Jul 2021 • Owen Convery, Lewis Smith, Yarin Gal, Adi Hanuka
Virtual Diagnostic (VD) is a deep learning tool that can be used to predict a diagnostic output.
no code implementations • 4 Jun 2021 • Lewis Smith, Joost van Amersfoort, Haiwen Huang, Stephen Roberts, Yarin Gal
ResNets constrained to be bi-Lipschitz, that is, approximately distance preserving, have been a crucial component of recently proposed techniques for deterministic uncertainty quantification in neural models.
2 code implementations • 22 Feb 2021 • Joost van Amersfoort, Lewis Smith, Andrew Jesson, Oscar Key, Yarin Gal
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 large datasets.
1 code implementation • 16 Feb 2021 • Mike Walmsley, Chris Lintott, Tobias Geron, Sandor Kruk, Coleman Krawczyk, Kyle W. Willett, Steven Bamford, Lee S. Kelvin, Lucy Fortson, Yarin Gal, William Keel, Karen L. Masters, Vihang Mehta, Brooke D. Simmons, Rebecca Smethurst, Lewis Smith, Elisabeth M. Baeten, Christine Macmillan
All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314, 000 galaxies.
no code implementations • 1 Jan 2021 • Joost van Amersfoort, Lewis Smith, Andrew Jesson, Oscar Key, Yarin Gal
Building on recent advances in uncertainty quantification using a single deep deterministic model (DUQ), we introduce variational Deterministic Uncertainty Quantification (vDUQ).
no code implementations • 17 Nov 2020 • Mizu Nishikawa-Toomey, Lewis Smith, Yarin Gal
We show that this novel architecture leads to improvements in accuracy when used for the galaxy morphology classification task on the Galaxy Zoo data set.
no code implementations • 7 Apr 2020 • Lewis Smith, Lisa Schut, Yarin Gal, Mark van der Wilk
'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.
2 code implementations • 4 Mar 2020 • Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal
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.
no code implementations • NeurIPS 2020 • 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 case in deep networks.
1 code implementation • 22 Dec 2019 • Angelos Filos, Sebastian Farquhar, Aidan N. Gomez, Tim G. J. Rudner, Zachary Kenton, Lewis Smith, Milad Alizadeh, Arnoud de Kroon, Yarin Gal
From our comparison we conclude that some current techniques which solve benchmarks such as UCI `overfit' their uncertainty to the dataset---when evaluated on our benchmark these underperform in comparison to simpler baselines.
no code implementations • 4 Oct 2019 • Gonzalo Mateo-Garcia, Silviu Oprea, Lewis Smith, Josh Veitch-Michaelis, Guy Schumann, Yarin Gal, Atılım Güneş Baydin, Dietmar Backes
Satellite imaging is a critical technology for monitoring and responding to natural disasters such as flooding.
1 code implementation • 17 May 2019 • Mike Walmsley, Lewis Smith, Chris Lintott, Yarin Gal, Steven Bamford, Hugh Dickinson, Lucy Fortson, Sandor Kruk, Karen Masters, Claudia Scarlata, Brooke Simmons, Rebecca Smethurst, Darryl Wright
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.
no code implementations • ICLR 2019 • Yarin Gal, Lewis Smith
Lastly, we demonstrate the defence on a cats-vs-dogs image classification task with a VGG13 variant.
no code implementations • 2 Jun 2018 • Yarin Gal, Lewis Smith
Lastly, we demonstrate the defence on a cats-vs-dogs image classification task with a VGG13 variant.
1 code implementation • 22 Mar 2018 • 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.