Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. A Bayesian Approach to Parameter Inference in Queueing Networks

    Wang, W., Casale, G. & Sutton, C., Aug 2016, In : ACM Transactions on Modeling and Computer Simulation. 27, 1, 26 p., 2.

    Research output: Contribution to journalArticle

  2. A Bayesian Network Model for Interesting Itemsets

    Fowkes, J. & Sutton, C., 4 Sep 2016, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD 2016). Riva del Garda, Italy: Springer, Cham, p. 410-425 16 p. (Lecture Notes in Computer Science ; vol. 9852).

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

  3. A Bayesian approach for structure learning in oscillating regulatory networks

    Trejo-Banos, D., Millar, A. & Sanguinetti, G., 14 Jul 2015, In : Bioinformatics. 31, 22, p. 3617-3624 8 p.

    Research output: Contribution to journalArticle

  4. A Biophysical model of long-term potentiation and synaptic tagging

    Barrett, A., Billings, G., Morris, R. G. M. & van Rossum, M. C. W., 2007.

    Research output: Contribution to conferencePoster

  5. A CONNECTIONIST MODEL OF PRELEXICAL PROCESSING IN SPOKEN WORD RECOGNITION

    LEVY, J., CHATER, N. & Shillcock, R., 1992, In : International Journal of Psychology. 27, 3-4, p. 63-63 1 p.

    Research output: Contribution to journalArticle

  6. A Case Study on Meta-generalising: A Gaussian Processes Approach

    Skolidis, G. & Sanguinetti, G., 1 Mar 2012, In : Journal of Machine Learning Research. 13, 1, p. 691-721 31 p.

    Research output: Contribution to journalArticle

  7. A Comparison of Frequentist and Bayesian Approaches to Latent Class Modelling of Susceptibility to Asthma and Patterns of Antibiotic Prescriptions in Early Life

    Belgrave, D., Bishop, C., Custovic, A., Simpson, A., Semic-Jusufagic, A., Pickles, A. & Buchan, I., 2011, Proceedings of the 26th International Workshop on Statistical Modelling. Valencia (Spain). p. 75-78 4 p.

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

  8. A Composable Strategy for Shredded Document Reconstruction

    Ranca, R. & Murray, I., 2013, Computer Analysis of Images and Patterns. Wilson, R., Hancock, E., Bors, A. & Smith, W. (eds.). Springer-Verlag GmbH, p. 324-331 8 p. (Lecture Notes in Computer Science; vol. 8048).

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

  9. A Convolutional Attention Network for Extreme Summarization of Source Code

    Allamanis, M., Peng, H. & Sutton, C., 24 Jun 2016, Proceedings of The 33rd International Conference on Machine Learning, PMLR. New York, United States: PMLR, Vol. 48. p. 2091-2100 10 p.

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

  10. A Deep and Tractable Density Estimator

    Uria, B., Murray, I. & Larochelle, H., 2014, Proceedings of The 31st International Conference on Machine Learning. Beijing, China: Journal of Machine Learning Research: Workshop and Conference Proceedings, Vol. 32. p. 467-475 9 p. (Journal of Machine Learning Research: Workshop and Conference Proceedings; vol. 32).

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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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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