
Explicit Regularisation in Gaussian Noise Injections
We study the regularisation induced in neural networks by Gaussian noise...
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Towards a Theoretical Understanding of the Robustness of Variational Autoencoders
We make inroads into understanding the robustness of Variational Autoenc...
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RelaxedResponsibility Hierarchical Discrete VAEs
Successfully training Variational Autoencoders (VAEs) with a hierarchy o...
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Inferring proximity from Bluetooth Low Energy RSSI with Unscented Kalman Smoothers
The Covid19 pandemic has resulted in a variety of approaches for managi...
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Neural Ensemble Search for Performant and Calibrated Predictions
Ensembles of neural networks achieve superior performance compared to st...
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Risk scoring calculation for the current NHSx contact tracing app
We consider how the NHS COVID19 application will initially calculate a ...
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Learning Bijective Feature Maps for Linear ICA
Separating highdimensional data like images into independent latent fac...
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Regularising Deep Networks with DGMs
Here we develop a new method for regularising neural networks where we l...
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Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders
In clustering we normally output one cluster variable for each datapoint...
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Disentangling Improves VAEs' Robustness to Adversarial Attacks
This paper is concerned with the robustness of VAEs to adversarial attac...
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On the marginal likelihood and crossvalidation
In Bayesian statistics, the marginal likelihood, also known as the evide...
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Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap
Increasingly complex datasets pose a number of challenges for Bayesian i...
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Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness
Machine learning (ML), artificial intelligence (AI) and other modern sta...
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A Framework for Adaptive MCMC Targeting Multimodal Distributions
We propose a new Monte Carlo method for sampling from multimodal distrib...
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Semiunsupervised Learning of Human Activity using Deep Generative Models
Here we demonstrate a new deep generative model for classification. We i...
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Principles of Bayesian Inference using General Divergence Criteria
When it is acknowledged that all candidate parameterised statistical mod...
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Principled Bayesian Minimum Divergence Inference
When it is acknowledged that all candidate parameterised statistical mod...
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Generalized Bayesian Updating and the LossLikelihood Bootstrap
In this paper, we revisit the weighted likelihood bootstrap and show tha...
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On Markov chain Monte Carlo methods for tall data
Markov chain Monte Carlo methods are often deemed too computationally in...
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Chris Holmes
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Professor of Biostatistics in Genomics in the Nuffield Department of Clinical Medicine and the Department of Statistics at the University of Oxford, Fellowship of St Anne's College, Oxford, Professor of Biostatistics and a Fellow of Lincoln College,