Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection

    Kivinen, J., Williams, C. K. I. & Heess, N., 2014, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics. Reykjavik, Iceland: Journal of Machine Learning Research: Workshop and Conference Proceedings, Vol. 33. p. 512-521 10 p.

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

  2. Visual Cortex as a General-Purpose Information-Processing Device

    Bednar, J. A., 2012, Computer Vision – ECCV 2012. Workshops and Demonstrations: Florence, Italy, October 7-13, 2012, Proceedings, Part I. Fusiello, A., Murino, V. & Cucchiara, R. (eds.). Springer Berlin Heidelberg, p. 480-485 6 p.

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

  3. Volume transmission as a new homeostatic mechanism

    Sweeney, Y. A., Hellgren-Kotaleski, J. & Hennig, M., 2013.

    Research output: Contribution to conferencePoster

  4. Weak Epistasis May Drive Adaptation in Recombining Bacteria

    Arnold, B. J., Gutmann, M., Grad, Y. H., Sheppard, S. K., Corander, J., Lipsitch, M. & Hanage, W. P., 1 Mar 2018, In : Genetics. 208, 3, p. 1247-1260 39 p.

    Research output: Contribution to journalArticle

  5. What can MaxEnt reveal about high-density recordings and what can high-density recordings reveal about MaxEnt?

    Panas, D., Maccione, A., Berdondini, L. & Hennig, M., 2011, In : BMC Neuroscience. 12, Supplement 1, 2 p., P146.

    Research output: Contribution to journalMeeting abstract

  6. What, if anything, are topological maps for?

    Wilson, S. P. & Bednar, J. A., 11 Feb 2015, In : Developmental neurobiology.

    Research output: Contribution to journalArticle

  7. When Training and Test Sets Are Different: Characterizing Learning Transfer

    Storkey, A., Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A. & Lawrence, ND., Dec 2008, Dataset Shift in Machine Learning. Cambridge: Yale University Press in association with the Museum of London, p. 3-28 26 p. (Neural Information Processing Series).

    Research output: Chapter in Book/Report/Conference proceedingChapter

  8. Why, when, and what: Analyzing Stack Overflow questions by topic, type, and code

    Allamanis, M. & Sutton, C., 2013, Mining Software Repositories (MSR), 2013 10th IEEE Working Conference on. Institute of Electrical and Electronics Engineers (IEEE), p. 53-56 4 p.

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

  9. Wide-band information transmission at the calyx of Held

    Hennig, M., Graham, B. P., Yang, Z., Postlethwaite, M. & Forsythe, I. D., Apr 2009, In : Neural Computation. 21, 4, p. 991-1017 27 p.

    Research output: Contribution to journalLetter

  10. Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio

    Jastrzębski, S., Kenton, Z., Arpit, D., Ballas, N., Fischer, A., Bengio, Y. & Storkey, A., Oct 2018, Proceedings of 27th International Conference on Artificial Neural Networks. Rhodes, Greece: Springer, Cham, p. 392-402 10 p.

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