Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Conference contribution › Research
  2. Inverting Supervised Representations with Autoregressive Neural Density Models

    Nash, C., Kushman, N. & Williams, C. K. I., 18 Apr 2019, Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics. Lawrence, N. & Reid, M. (eds.). PMLR, Vol. 89. 10 p. (Proceedings of Machine Learning Research; vol. 89).

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

  3. Isoelastic Agents and Wealth Updates in Machine Learning Markets

    Storkey, A., Millin, J. & Geras, K., 27 Jun 2012, Proceedings of the 29th International Conference on Machine Learning (ICML 2012). 8 p.

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

  4. Joint Parsing and Semantic Role Labeling

    Sutton, C. & McCallum, A., 1 Jun 2005, Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005). Ann Arbor, Michigan: Association for Computational Linguistics, p. 225-228 4 p.

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

  5. Kick-starting GPLVM Optimization via a Connection to Metric MDS

    Bitzer, S. & Williams, C. K. I., 2010, Proceedings of the NIPS 2010 workshop on Challenges of Data Visualization. 6 p.

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

  6. Known Unknowns: Novelty Detection in Condition Monitoring

    Quinn, J. A. & Williams, C. K. I., 2007, Pattern Recognition and Image Analysis: Third Iberian Conference, IbPRIA 2007, Girona, Spain, June 6-8, 2007, Proceedings, Part I. Martí, J., Benedí, J. M., Mendonça, A. M. & Serrat, J. (eds.). Springer Berlin Heidelberg, p. 1-6 6 p. (Lecture Notes in Computer Science; vol. 4477).

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

  7. Large Developing Axonal Arbors Using a Distributed and Locally-Reprogrammable Address-Event Receiver

    Bamford, S., Murray, A. & Willshaw, D. J., 1 Jun 2008, IEEE International Joint Conference on Neural Networks (IJCNN). p. 1464-1471 8 p.

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

  8. Large-Scale Study of Curiosity-Driven Learning

    Burda, Y., Edwards, H., Pathak, D., Storkey, A., Darrell, T. & Efros, A. A., 9 May 2019, 7th International Conference on Learning Representations (ICLR 2019). p. 1-17 17 p.

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

  9. Latent Bayesian melding for integrating individual and population models

    Zhong, M., Goddard, N. & Sutton, C., 2015, Advances in Neural Information Processing Systems 28 (NIPS 2015). p. 3617-3625 9 p.

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

  10. Latent Variables, Topographic Mappings and Data Visualization

    Bishop, C., 1998, Neural Nets WIRN VIETRI-97: Proceedings of the 9th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, 22–24 May 1997. Marinaro, M. & Tagliaferri, R. (eds.). Springer London, p. 3-32 30 p. (Perspectives in Neural Computing).

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

  11. Learning Continuous Semantic Representations of Symbolic Expressions

    Allamanis, M., Chanthirasegaran, P., Kohli, P. & Sutton, C., 11 Aug 2017, The 34th International Conference on Machine Learning (ICML 2017). Sydney, Australia: PMLR, Vol. 70. p. 80-88 9 p. (Proceedings of Machine Learning Research; vol. 70).

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