Neural networks and machine learning
NNM
21 July 1997 to 19 December 1997
Programme theme
Research in machine learning has advanced significantly in recent years, stimulated in part by the emergence of a range of successful, large-scale applications. Examples include optical character recognition, classification of sleep stages from EEG signals, cervical smear screening, and real-time tokamak plasma control. At the same time there have been many impressive developments in the theoretical foundations of this field, arising from several complementary approaches. Concepts from statistical pattern recognition have been used to formulate a general framework for machine learning based on statistical inference. Parallel developments in computational learning theory have led to a characterisation of computational and sample-size requirements for learning problems, while also resulting in powerful new algorithms. In addition, concepts from information theory, differential geometry and statistical mechanics have been exploited to give alternative insights into neural networks. The principal aims of this programme are to promote greater inter-disciplinary collaboration between researchers with different theoretical perspectives, to strive for a more unified mathematical framework for neural networks and machine learning, and to stimulate the development of new algorithms for practical applications.
Click here to download the programme's final scientific report
Preprints
| Title | Year | Programme | Publication Date |
|---|---|---|---|
On chain graph models for description of conditional independence structuresAuthors: M Studeny, RR Bouckaert |
1997 | NNM | 21 October 2016 |
A unified theory of density models and auto-encodersAuthors: SP Luttrell |
1997 | NNM | 21 October 2016 |
Bayesian classification with Gaussian processesAuthors: CKI Williams, D Barber |
1997 | NNM | 21 October 2016 |
Chain graphs and symmetric associationsAuthors: TS Richardson |
1997 | NNM | 21 October 2016 |
An importance sampler for graphical Gaussian model inferenceAuthors: A Roverato, J Whittaker |
1997 | NNM | 21 October 2016 |
Organisers
- Christopher Bishop Microsoft (UK)
- Mahesan Niranjan University of Southampton
- Leslie Valiant Harvard University
Supported By
