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07/29/2021
Quantifying Uncertainty for Machine Learning Based Diagnostic
Virtual Diagnostic (VD) is a deep learning tool that can be used to pred...
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06/04/2021
Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective
ResNets constrained to be bi-Lipschitz, that is, approximately distance ...
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02/22/2021
Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression
We propose a new model that estimates uncertainty in a single forward pa...
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11/17/2020
Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders
The growth in the number of galaxy images is much faster than the speed ...
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04/07/2020
Capsule Networks – A Probabilistic Perspective
'Capsule' models try to explicitly represent the poses of objects, enfor...
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03/04/2020
Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network
We propose a method for training a deterministic deep model that can fin...
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02/10/2020
Try Depth Instead of Weight Correlations: Mean-field is a Less Restrictive Assumption for Deeper Networks
We challenge the longstanding assumption that the mean-field approximati...
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12/22/2019
A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks
Evaluation of Bayesian deep learning (BDL) methods is challenging. We of...
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10/04/2019
Flood Detection On Low Cost Orbital Hardware
Satellite imaging is a critical technology for monitoring and responding...
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05/17/2019
Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
We use Bayesian convolutional neural networks and a novel generative mod...
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06/02/2018
Idealised Bayesian Neural Networks Cannot Have Adversarial Examples: Theoretical and Empirical Study
We prove that idealised discriminative Bayesian neural networks, capturi...
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03/22/2018