Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Conference contribution › Research
  2. Scientific data mining, integration, and visualization

    Mann, B., Williams, R., Atkinson, M., Brodlie, K., Storkey, A. & Williams, C. K. I., 2002, Report of workshop held at the National e-Science Institute. 24 p.

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

  3. Segmenting video into classes of algorithm-suitability

    Mac Aodha, O., Brostow, G. J. & Pollefeys, M., 5 Aug 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics Engineers (IEEE), p. 1054-1061 8 p.

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

  4. Segmenting white matter structure from diffusion MRI

    Piatkowski, J. P., Storkey, A. & Bastin, M., 2012, ICML Workshop on Statistics, Machine Learning and Neuroscience.

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

  5. Self-Organization of Innate Face Preferences: Could Genetics Be Expressed Through Learning?

    Bednar, J. A. & Miikkulainen, R., 2000, Proceedings of the Seventeenth National Conference on Artificial Intelligence. AAAI Press, p. 117-122

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

  6. Self-Organization of Orientation Maps, Lateral Connections, and Dynamic Receptive Fields in the Primary Visual Cortex

    Sirosh, J., Miikkulainen, R. & Bednar, J. A., 1996, Proceedings of the International Conference on Artificial Neural Networks. Sirosh, J., Miikkulainen, R. & Choe, Y. (eds.). Berlin: Springer Japan, p. 1147-1152 6 p.

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

  7. Self-Organization of Spatiotemporal Receptive Fields and laterally connected direction and orientation maps

    Bednar, J. A. & Miikkulainen, R., 2003, Neurocomputing. Thoemmes Press, p. 52-54 3 p.

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

  8. Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models

    Zhang, Y. & Sutton, C., 2014, Advances in Neural Information Processing Systems 27. Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D. & Weinberger, K. Q. (eds.). Curran Associates Inc, p. 10-18 9 p.

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

  9. Sequence-to-point learning with neural networks for non-intrusive load monitoring

    Zhang, C., Zhong, M., Wang, Z., Goddard, N. & Sutton, C., 7 Feb 2018, Proceedings for Thirty-Second AAAI Conference on Artificial Intelligence. New Orleans, Louisiana, USA: AAAI Press, p. 2604-2611 8 p.

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

  10. Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows

    Papamakarios, G., C, D. & Murray, I., 25 Apr 2019, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019. Naha, Okinawa, Japan: PMLR, Vol. 89. p. 837-848 12 p.

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

  11. Sex, Flies and no Videotape

    Armstrong, D., Baker, D. A., Heward, J. A. & Lukins, T. C., 2005, 5th International Conference on Methods and Techniques in Behavioral Research.

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