Learn More
We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed genera-tive 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 optimisation of a variational lower(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)
Automated discovery of early visual concepts from raw image data is a major open challenge in AI research. Addressing this problem, we propose an unsupervised approach for learning disentangled representations of the underlying factors of variation. We draw inspiration from neuroscience, and show how this can be achieved in an unsupervised generative model(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)
Humans have an impressive ability to reason about new concepts and experiences from just a single example. In particular, humans have an ability for one-shot generalization: an ability to encounter a new concept, understand its structure , and then be able to generate compelling alternative variations of the concept. We develop machine learning systems with(More)