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We marry ideas from deep neural networks and approximate Bayesian inference to derive a gen-eralised class of deep, directed generative models , endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent an approximate posterior distribution and uses this for optimisa-tion of a variational(More)
The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and develop new models that allow for effective generalisation(More)
The use of L 1 regularisation for sparse learning has generated immense research interest , with many successful applications in diverse areas such as signal acquisition, image coding, genomics and collaborative filtering. While existing work highlights the many advantages of L 1 methods, in this paper we find that L 1 regularisation often dramatically(More)
— The classification of protein sequences into families is an important tool in the annotation of structural and functional properties to newly discovered proteins. We present a classification system using pattern recognition techniques to create a numerical vector representation of a protein sequence and then classify the sequence into a number of given(More)
Nonparametric Bayesian models provide a framework for flexible probabilistic modelling of complex datasets. Unfortunately, the high-dimensional averages required for Bayesian methods can be slow, especially with the unbounded representations used by nonparametric models. We address the challenge of scaling Bayesian inference to the increasingly large(More)
Introduction Motivation: Analysis of high-dimensional categorical data is essential in applications such as recommender systems, econometrics, social sciences, and medical diagnostics. Such analysis can be carried out using latent Gaussian models, which include multinomial logistic regression, multi-class Gaussian process classification, categorical factor(More)
This paper studies issues relating to the parameterization of probability distributions over binary data sets. Several such param-eterizations of models for binary data are known, including the Ising, generalized Ising, canonical and full parameterizations. We also discuss a parameterization that we call the " spectral parameterization " , which has(More)