A common view is that up to 80% of work on a data mining project is involved in data understanding and data preparation, yet machine learning has not focused very much on these topics. This talk will describe the Artificial Intelligence for Data Analytics (AIDA) project that is starting in the Alan Turing Institute. AIDA will make use statistical and machine learning methods, and approaches from semantic technologies, to address problems of data parsing, data understanding, data cleaning, data integration and data restructuring.

Joint work with James Geddes, Zoubin Ghahramani, Ian Horrocks and Charles Sutton at the Alan Turing Institute.

If I have time I will also describe some recent work on Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single Image (joint with Lukasz Romaszko, Pol Moreno and Pushmeet Kohli). We develop an inverse graphics approach to the problem of scene understanding, obtaining a rich representation that includes descriptions of the objects in the scene and their spatial layout, as well as global latent variables like the camera parameters and lighting. The framework's stages include object detection, the prediction of the camera and lighting variables, and prediction of object-specific variables (shape, appearance and pose). This acts like the encoder of an autoencoder, with graphics rendering as the decoder. Importantly the scene representation is interpretable and is of variable dimension to match the detected number of objects plus the global variables. For the prediction of the camera latent variables we introduce a novel architecture termed Probabilistic HoughNets (PHNs), which provides a principled approach to combining information from multiple detections. We demonstrate the quality of the reconstructions obtained quantitatively on synthetic data, and qualitatively on real scenes.

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Bio:

Chris Williams is Professor of Machine Learning in the School of Informatics, University of Edinburgh, and University Liaison Director for the Alan Turing Institute. He is interested in a wide range of theoretical and practical issues in machine learning, statistical pattern recognition, probabilistic graphical models and computer vision. This includes theoretical foundations, the development of new models and algorithms, and applications. His main areas of research are in visual object recognition and image understanding, models for understanding time-series, unsupervised learning, and Gaussian processes.

He obtained his MSc (1990) and PhD (1994) at the University of Toronto, under the supervision of Geoff Hinton. He was a member of the Neural Computing Research Group at Aston University from 1994 to 1998, and has been at the University of Edinburgh since 1998. He was program co-chair of NIPS in 2009, and is on the editorial boards of the Journal of Machine Learning Research and Proceedings of the Royal Society A.