127 citations · 298 across the 18 of their papers we have counts for
6 papers · 1 filter
Mosquito Detection with Neural Networks: The Buzz of Deep Learning
Ivan Kiskin, Bernardo Pérez Orozco, Theo Windebank +4
Many real-world time-series analysis problems are characterised by scarce data. Solutions typically rely on hand-crafted features extracted from the time or frequency domain allied…
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
Tom Gunter, Michael A. Osborne, Roman Garnett +2
We propose a novel sampling framework for inference in probabilistic models: an active learning approach that converges more quickly (in wall-clock time) than Markov chain Monte Ca…
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes
Yves-Laurent Kom Samo, Stephen Roberts
In this paper we propose the first non-parametric Bayesian model using Gaussian Processes to make inference on Poisson Point Processes without resorting to gridding the domain or t…
Automated Machine Learning on Big Data using Stochastic Algorithm Tuning
Thomas Nickson, Michael A Osborne, Steven Reece +1
We introduce a means of automating machine learning (ML) for big data tasks, by performing scalable stochastic Bayesian optimisation of ML algorithm parameters and hyper-parameters…
Efficient Bayesian Nonparametric Modelling of Structured Point Processes
Tom Gunter, Chris Lloyd, Michael A. Osborne +1
This paper presents a Bayesian generative model for dependent Cox point processes, alongside an efficient inference scheme which scales as if the point processes were modelled inde…
Efficient Bayesian Community Detection using Non-negative Matrix Factorisation
Ioannis Psorakis, Stephen Roberts, Ben Sheldon
Identifying overlapping communities in networks is a challenging task. In this work we present a novel approach to community detection that utilises the Bayesian non-negative matri…