Projects per year
Understanding data, models and predictions is important for machine learning applications. Due to the limitations of our spatial perception and intuition, analysing high-dimensional data is inherently difficult. Furthermore, black-box models achieving high predictive accuracy are widely used, yet the logic behind their predictions is often opaque. Use of textualisation - a natural language narrative of selected phenomena - can tackle these shortcomings. When extended with argumentation theory we could envisage machine learning models and predictions arguing persuasively for their choices.
|Title of host publication||26th International Joint Conference on Artificial Intelligence, IJCAI 2017|
|Subtitle of host publication||Proceedings of a meeting held 19-25 August 2017, Melbourne, Australia.|
|Publisher||International Joint Conferences on Artificial Intelligence|
|Number of pages||2|
|Publication status||Published - 25 Aug 2017|
|Event||26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia|
Duration: 19 Aug 2017 → 25 Aug 2017
|Conference||26th International Joint Conference on Artificial Intelligence, IJCAI 2017|
|Period||19/08/17 → 25/08/17|
- Digital Health
Craddock, I. J., Coyle, D. T., Flach, P. A., Kaleshi, D., Mirmehdi, M., Piechocki, R. J., Stark, B. H., Ascione, R., Ashburn, A. M., Burnett, M. E., Damen, D., Gooberman-Hill, R. J. S., Harwin, W. S., Hilton, G., Holderbaum, W., Holley, A. P., Manchester, V. A., Meller, B. J., Stack, E. & Gilchrist, I. D.
1/10/13 → 30/09/18
Project: Research, Parent