Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. 2019
  2. Exploration by random network distillation

    Burda, Y., Edwards, H., Storkey, A. & Klimov, O., 2019. 17 p.

    Research output: Contribution to conferencePaper

  3. Gene Regulatory Network Inference: An Introductory Survey

    Huynh-Thu, V. A. & Sanguinetti, G., 2019, Gene Regulatory Networks: Methods and Protocols. Sanguinetti, G. & Huynh-Thu, V. A. (eds.). New York, NY: Springer New York LLC, p. 1-23 23 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  4. Gene Regulatory Networks: Methods and Protocols

    Sanguinetti, G. (ed.) & Huynh-Thu, V. A. (ed.), 2019, Humana Press. 285 p. (Methods in Molecular Biology Series Volume; vol. 1883)

    Research output: Book/ReportBook

  5. How to train your MAML

    Antoniou, A., Edwards, H. & Storkey, A., 2019. 11 p.

    Research output: Contribution to conferencePaper

  6. Large-Scale Study of Curiosity-Driven Learning

    Burda, Y., Edwards, H., Pathak, D., Storkey, A., Darrell, T. & Efros, A. A., 2019. 15 p.

    Research output: Contribution to conferencePaper

  7. On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length

    Jastrzębski, S., Kenton, Z., Ballas, N., Fischer, A., Bengio, Y. & Storkey, A., 2019. 19 p.

    Research output: Contribution to conferencePaper

  8. RKappa: Software for Analyzing Rule-Based Models

    Sorokin, A., Sorokina, O. & Douglas Armstrong, J., 2019, Modeling Biomolecular Site Dynamics: Methods and Protocols. Hlavacek, W. S. (ed.). New York, NY: Springer New York LLC, p. 363-390 28 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  9. Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3

    Huynh-Thu, V. A. & Sanguinetti, G., 2019, Gene Regulatory Networks: Methods and Protocols. Sanguinetti, G. & Huynh-Thu, V. A. (eds.). New York, NY: Springer New York LLC, p. 217-233 17 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  10. Using BRIE to Detect and Analyze Splicing Isoforms in scRNA-Seq Data

    Huang, Y. & Sanguinetti, G., 2019, Computational Methods for Single-Cell Data Analysis. Yuan, G-C. (ed.). New York, NY: Springer New York LLC, p. 175-185 11 p. (Methods in Molecular Biology; vol. 1935).

    Research output: Chapter in Book/Report/Conference proceedingChapter

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