Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Inference and Learning in Networks of Queues

    Sutton, C. & Jordan, M. I., 2010, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2010. Journal of Machine Learning Research: Workshop and Conference Proceedings, p. 796-813 8 p. (JMLR Workshop and Conference Proceedings; vol. 9).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  2. Improved Dynamic Schedules for Belief Propagation

    Sutton, C. & McCallum, A., 2007, Proceedings of the Twenty-Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-07). Corvallis, Oregon: AUAI Press, p. 376-383 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  3. Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data

    Sutton, C., McCallum, A. & Rohanimanesh, K., 1 May 2007, In : Journal of Machine Learning Research. 8, p. 693-723 31 p.

    Research output: Contribution to journalArticle

  4. An Introduction to Conditional Random Fields

    Sutton, C. & McCallum, A., 2012, In : Foundations and Trends in Machine Learning. 4, 4, p. 267-373 109 p.

    Research output: Contribution to journalArticle

  5. Piecewise training for structured prediction

    Sutton, C. & McCallum, A., Dec 2009, In : Machine Learning. 77, 2-3, p. 165-194 30 p.

    Research output: Contribution to journalArticle

  6. Local Training and Belief Propagation

    Sutton, C. & Minka, T., Aug 2006, Microsoft Research, 10 p. (Microsoft Research Technical Reports; no. MSR-TR-2006-121).

    Research output: Working paper

  7. Screening of a Combinatorial Homing Peptide Library for Selective Cellular Delivery

    Svensen, N., Diaz-Mochon, J. J., Dhaliwal, K., Planonth, S., Dewar, M., Armstrong, J. D. & Bradley, M., Jun 2011, In : Angewandte Chemie International Edition. 50, 27, p. 6133-6136 4 p.

    Research output: Contribution to journalArticle

  8. Volume transmission as a new homeostatic mechanism

    Sweeney, Y. A., Hellgren-Kotaleski, J. & Hennig, M., 2013.

    Research output: Contribution to conferencePoster

  9. A Diffusive Homeostatic Signal Maintains Neural Heterogeneity and Responsiveness in Cortical Networks

    Sweeney, Y., Hellgren Kotaleski, J. & Hennig, M. H., Jul 2015, In : PLoS Computational Biology. 11, 7, e1004389.

    Research output: Contribution to journalArticle

  10. Homeostatic intrinsic plasticity, neural heterogeneity and memory maintenance

    Sweeney, Y., Hellgren-Kotaleski, J. & Hennig, M., Dec 2015, In : BMC Neuroscience. 16, 1, p. 1-2 2 p.

    Research output: Contribution to journalArticle