Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Conference contribution › Research
  2. Overcoming Occlusion with Inverse Graphics

    Moreno, P., Williams, C. K. I., Nash, C. & Kohli, P., 16 Nov 2016, Computer Vision: ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III. Hua, G. & Jégou, H. (eds.). Cham: Springer International Publishing, p. 170-185 16 p. (Lecture Notes in Computer Science (LNCS); vol. 9915).

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

  3. Parameter-Free Probabilistic API Mining across GitHub

    Fowkes, J. & Sutton, C., 1 Nov 2016, FSE 2016: ACM SIGSOFT International Symposium on the Foundations of Software Engineering. Seattle, United States: ACM, p. 254-265 12 p.

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

  4. Patch Based Synthesis for Single Depth Image Super-Resolution

    Mac Aodha, O., Campbell, N. D. F., Nair, A. & Brostow, G. J., 13 Oct 2012, Computer Vision -- ECCV 2012. Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y. & Schmid, C. (eds.). Berlin, Heidelberg: Springer Berlin Heidelberg, p. 71-84 14 p. (Lecture Notes in Computer Science (LNCS); vol. 7574).

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

  5. Pattern-Generator-Driven Development In Self-Organizing Models

    Bednar, J. A. & Miikkulainen, R., 1998, Computational Neuroscience: Trends in Research, 1998. Bower, J. M. (ed.). Plenum Press, p. 317-323 7 p.

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

  6. Perceptions and expectation about Learning Analytics from a Brazilian Higher Education Institution

    Pontual, T., Ferreira Mello, R., Lins, R., Diniz, J., Tsai, Y-S. & Gasevic, D., 2 Dec 2019, (Accepted/In press) The 10th International Learning Analytics & Knowledge Conference. ACM, 10 p.

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

  7. Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

    Radu, V., Kaszyk, J., Wen, Y., Turner, J., Cano, J., Crowley, E., Franke, B., Storkey, A. & O'Boyle, M., 15 Aug 2019, (Accepted/In press) 2019 IEEE International Symposium on Workload Characterization (IISWC). IEEE, 11 p.

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

  8. Physiological Monitoring with Factorial Switching Linear Dynamical Systems

    Quinn, J. A. & Williams, C. K. I., Aug 2011, Bayesian Time Series Models. Barber, D., Cemgil, A. T. & Chiappa, S. (eds.). Cambridge University Press, p. 182-204 23 p.

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

  9. Piecewise Pseudolikelihood for Efficient Training of Conditional Random Fields

    Sutton, C. & McCallum, A., 2007, Proceedings of the 24th International Conference on Machine Learning. New York, NY, USA: ACM, p. 863-870 8 p.

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

  10. Piecewise Training for Undirected Models

    Sutton, C. & McCallum, A., 2005, Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05). Arlington, Virginia: AUAI Press, p. 568-575 8 p.

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

  11. Piecewise Training with Parameter Independence Diagrams: Comparing Globally- and Locally-trained Linear-chain CRFs

    McCallum, A. & Sutton, C., 2004, NIPS Workshop on Learning with Structured Outputs. NIPS Foundation, 10 p.

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