Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. 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

  2. A Family of Computationally Efficient and Simple Estimators for Unnormalized Statistical Models

    Pihlaja, M., Gutmann, M. & Hyvärinen, A., 2010, Proc. Conf. on Uncertainty in Artificial Intelligence (UAI). Corvallis, Oregon: AUAI Press, p. 442-449 8 p.

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

  3. A Framework for Characterizing an Economy by its Energy and Socio-Economic Activities

    Roberts, S., Axon, C., Foran, B., Goddard, N. & Warr, B. S., Feb 2015, In : Sustainable Cities and Society. 14, p. 99-113 15 p.

    Research output: Contribution to journalArticle

  4. A Framework for Evaluating Approximation Methods for Gaussian Process Regression

    Chalupka, K., Williams, C. K. I. & Murray, I., 2013, In : Journal of Machine Learning Research. 14, p. 333-350 18 p.

    Research output: Contribution to journalArticle

  5. A Framework for the Quantitative Evaluation of Disentangled Representations

    Eastwood, C. & Williams, C. K. I., 3 May 2018, Sixth International Conference on Learning Representations (ICLR 2018). 15 p.

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

  6. A Frank mixture copula family for modeling higher-order correlations of neural spike counts

    Onken, A. & Obermayer, K., 2009, In : Journal of Physics: Conference Series. 197, 1, p. 1-10 10 p., 12019.

    Research output: Contribution to journalArticle

  7. A Generative Model for Parts-based Object Segmentation

    Eslami, S. M. A. & Williams, C. K. I., 2012, Advances in Neural Information Processing Systems 25. Bartlett, P., Pereira, F. C. N., Burges, C. J. C., Bottou, L. & Weinberger, K. Q. (eds.). p. 100-107 8 p.

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

  8. A Hierarchical Generative Model of Recurrent Object-Based Attention in the Visual Cortex

    Reichert, D. P., Series, P. & Storkey, A. J., 2011, ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2011, PT I. Honkela, T., Duch, W., Girolami, M. & Kaski, S. (eds.). BERLIN: Springer-Verlag Berlin Heidelberg, p. 18-25 8 p. (Lecture Notes in Computer Science; vol. 6791).

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

  9. A Hierarchical Switching Linear Dynamical System Applied to the Detection of Sepsis in Neonatal Condition Monitoring

    Stanculescu, I., Williams, C. K. I. & Freer, Y., 2014, Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014). 10 p.

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

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