Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. 2019
  2. Poster: Space and Time Optimal DNN Primitive Selection with Integer Linear Programming

    Wen, Y., Anderson, A., Radu, V., O'Boyle, M. & Gregg, D., 7 Nov 2019, 2019 28th International Conference on Parallel Architectures and Compilation Techniques (PACT). IEEE, p. 488-489 2 p.

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

  3. Neural Field Models for Latent State Inference: Application to Large-Scale Neuronal Recordings

    Rule, M. E., Schnoerr, D., Hennig, M. & Sanguinetti, G., 25 Oct 2019, (Accepted/In press) In : PLoS Computational Biology. 21 p.

    Research output: Contribution to journalArticle

  4. Complexity Leadership in Learning Analytics: Drivers, Challenges, and Opportunities

    Tsai, Y-S., Poquet, O., Gašević, D., Dawson, S. & Pardo, A., 21 Oct 2019, In : British Journal of Educational Technology. 50, 6, p. 2839–2854

    Research output: Contribution to journalArticle

  5. Dissecting the Shared and Context-Dependent Pathways Mediated by the p140Cap Adaptor Protein in Cancer and in Neurons

    Chapelle, J., Sorokina, O., McLean, C., Salemme, V., Alfieri, A., Angelini, C., Morellato, A., Adrait, A., Menna, E., Matteoli, M., Couté, Y., Ala, U., Turco, E., Defilippi, P. & Armstrong, J. D., 15 Oct 2019, In : Frontiers in Cell and Developmental Biology. 7, 19 p., 222.

    Research output: Contribution to journalArticle

  6. Categorical encoding of decision variables in orbitofrontal cortex

    Onken, A., Xie, J., Panzeri, S. & Padoa-Schioppa, C., 14 Oct 2019, In : PLoS Computational Biology. 15, 10, 27 p., e1006667.

    Research output: Contribution to journalArticle

  7. Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics

    Tsai, Y-S., Perrotta, C. & Gasevic, D., 2 Oct 2019, (Accepted/In press) In : Assessment & Evaluation in Higher Education. 23 p.

    Research output: Contribution to journalArticle

  8. Scalable Extreme Deconvolution

    Ritchie, J. & Murray, I., 2 Oct 2019, (Accepted/In press) Proceedings of the Second Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019). Neural Information Processing Systems, 7 p.

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

  9. Assessing the validity of a learning analytics expectation instrument: A multinational study

    Whitelock-Wainwright, A., Gasevic, D., Tsai, Y-S., Drachsler, H., Scheffel, M., Muñoz-Merino, P., Tammets, K. & Delgado Kloos, C., 1 Oct 2019, (Accepted/In press) In : Journal of Computer Assisted Learning. 53 p.

    Research output: Contribution to journalArticle

  10. The Student Expectations of Learning Analytics Questionnaire (SELAQ)

    Whitelock-Wainwright, A., Gasevic, D., Tejeiro, R., Tsai, Y-S. & Bennett, K., Oct 2019, In : Journal of Computer Assisted Learning. 35, 5, p. 633-666 34 p.

    Research output: Contribution to journalArticle

  11. Parametric copula models reveal neuronal and behavioral time-dependent relationships in primary visual cortex

    Kudryashova, N., Amvrosiadis, T., Dupuy, N., Rochefort, N. & Onken, A., 19 Sep 2019. 1 p.

    Research output: Contribution to conferencePoster

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