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02/06/2023
Spatial Functa: Scaling Functa to ImageNet Classification and Generation
Neural fields, also known as implicit neural representations, have emerg...
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03/07/2022
Regularising for invariance to data augmentation improves supervised learning
Data augmentation is used in machine learning to make the classifier inv...
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06/28/2021
Laplace Redux – Effortless Bayesian Deep Learning
Bayesian formulations of deep learning have been shown to have compellin...
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04/11/2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
Marginal-likelihood based model-selection, even though promising, is rar...
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01/26/2021
Generalized Doubly Reparameterized Gradient Estimators
Efficient low-variance gradient estimation enabled by the reparameteriza...
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08/19/2020
Improving predictions of Bayesian neural networks via local linearization
In this paper we argue that in Bayesian deep learning, the frequently ut...
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06/05/2019
Interpretable and Differentially Private Predictions
Interpretable predictions, where it is clear why a machine learning mode...
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10/26/2018
Resampled Priors for Variational Autoencoders
We propose Learned Accept/Reject Sampling (LARS), a method for construct...
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08/16/2018
Learning Invariances using the Marginal Likelihood
Generalising well in supervised learning tasks relies on correctly extra...
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05/24/2018
Decision-Theoretic Meta-Learning: Versatile and Efficient Amortization of Few-Shot Learning
This paper develops a general framework for data efficient and versatile...
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05/04/2018
Automatic Estimation of Modulation Transfer Functions
The modulation transfer function (MTF) is widely used to characterise th...
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06/01/2017
Discriminative k-shot learning using probabilistic models
This paper introduces a probabilistic framework for k-shot image classif...
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06/15/2016