Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Sparse instrumental variables (SPIV) for genome-wide studies

    Agakov, F. V., McKeigue, P., Krohn, J. & Storkey, A., 1 Jan 2010, Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  2. Discriminative Mixtures of Sparse Latent Fields for Risk Management

    Agakov, F. V., Orchard, P. & Storkey, A. J., 2012, Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12). Lawrence, N. D. & Girolami, M. A. (eds.). Vol. 22. p. 10-18 9 p.

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

  3. Using Machine Learning to Focus Iterative Optimization

    Agakov, F., Bonilla, E., Cavazos, J., Franke, B., Fursin, G., O'Boyle, M. F. P., Thomson, J., Toussaint, M. & Williams, C. K. I., 2006, Proceedings of the International Symposium on Code Generation and Optimization. Washington, DC, USA: Institute of Electrical and Electronics Engineers (IEEE), p. 295-305 11 p. (CGO '06).

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

  4. Synaptic interactome mining reveals p140Cap as a new hub for PSD proteins involved in psychiatric and neurological disorders

    Alfieri, A., Sorokina, O., Adrait, A., Angelini, C., Russo, I., Morellato, A., Matteoli, M., Menna, E., Erba, E. B., McLean, C., Armstrong, J. D., Ala, U., Buxbaum, J. D., Brusco, A., Couté, Y., De Rubeis, S., Turco, E. & Defilippi, P., 30 Jun 2017, In : Frontiers in Molecular Neuroscience. 10, p. 1-15 15 p., 212.

    Research output: Contribution to journalArticle

  5. Effects of ambient luminance on retinal information coding

    Alizadeh, A., Onken, A., Mutter, M., Münch, T. & Panzeri, S., 14 Sep 2017. 2 p.

    Research output: Contribution to conferenceAbstract

  6. Mining Idioms from Source Code

    Allamanis, M. & Sutton, C., 2014, Proceedings of the 22Nd ACM SIGSOFT International Symposium on Foundations of Software Engineering. New York, NY, USA: ACM, p. 472-483 12 p.

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

  7. Suggesting Accurate Method and Class Names

    Allamanis, M., Barr, E. T., Bird, C. & Sutton, C., 2015, Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering. ACM, p. 38-49 12 p.

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

  8. Mining source code repositories at massive scale using language modeling

    Allamanis, M. & Sutton, C., 2013, Mining Software Repositories (MSR), 2013 10th IEEE Working Conference on. Institute of Electrical and Electronics Engineers (IEEE), p. 207-216 10 p.

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

  9. Mining Semantic Loop Idioms

    Allamanis, M., Barr, E. T., Bird, C., Devanbu, P., Marron, M. & Sutton, C., 1 Jul 2018, In : IEEE Transactions on Software Engineering. 44, 7, 18 p.

    Research output: Contribution to journalArticle

  10. 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. (Proceedings of Machine Learning Research; vol. 48).

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

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