Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Conference contribution › Research
  2. 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

  3. 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

  4. 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

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

    Pontual Falcão, T., Ferreira Mello, R., Lins Rodrigues, R., Regueira Bast Diniz, J., Tsai, Y-S. & Gasevic, D., 23 Mar 2020, LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. Association for Computing Machinery (ACM), p. 240-249 10 p.

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

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

    Radu, V., Kaszyk, K., Wen, Y., Turner, J., Cano, J., Crowley, E., Franke, B., Storkey, A. & O'Boyle, M., 19 Mar 2020, 2019 IEEE International Symposium on Workload Characterization (IISWC). Orlando, FL, USA: Institute of Electrical and Electronics Engineers (IEEE), p. 24-34 11 p.

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

  7. 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

  8. 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

  9. 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

  10. 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

  11. Policy Matters: Expert Recommendations for Learning Analytics Policy

    Scheffel, M., Tsai, Y-S., Gasevic, D. & Drachsler, H., 9 Sep 2019, Transforming Learning with Meaningful Technologies. EC-TEL 2019.. Springer, p. 510-524 15 p. (Lecture Notes in Computer Science; vol. 11722).

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