Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Utilising disaggregated energy data in feedback designs – the IDEAL project

    Goddard, N., Pullinger, M., Webb, L., Farrow, E., Farrow, E., Kilgour, J., Morgan, E. & Moore, J., Jul 2016, TEDDINET Energy Feedback Symposium 2016. p. 74-77 4 p.

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

  2. VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning

    Srivastava, A., Valkov, L., Russell, C., Gutmann, M. & Sutton, C., 9 Dec 2017, Advances in Neural Information Processing Systems 30 (NIPS 2017). Long Beach, CA, USA: Curran Associates Inc, p. 3308-3318 18 p.

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

  3. VIBES: A Variational Inference Engine for Bayesian Networks

    Bishop, C. M., Spiegelhalter, D. J. & Winn, J., 2002, Advances in Neural Information Processing Systems 15 (NIPS 2002). 8 p.

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

  4. Validating a standardised test battery for synesthesia: Does the Synesthesia Battery reliably detect synesthesia?

    Carmichael, D. A., Down, M. P., Shillcock, R. C., Eagleman, D. M. & Simner, J., May 2015, In : Consciousness and Cognition. 33, p. 375-385 11 p.

    Research output: Contribution to journalArticle

  5. Validity conditions for moment closure approximations in stochastic chemical kinetics

    Schnoerr, D., Sanguinetti, G. & Grima, R., 1 Jan 2014, In : Journal of Chemical Physics. 141, 8, 084103.

    Research output: Contribution to journalArticle

  6. Variational Bayesian Model Selection for Mixture Distributions

    Corduneanu, A. & Bishop, C. M., 2001, Proceedings Eighth International Conference on Artificial Intelligence and Statistics. Morgan Kaufmann, p. 27-34 8 p.

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

  7. Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations

    Zammit-Mangion, A., Sanguinetti, G. & Kadirkamanathan, V., 1 Jul 2012, In : IEEE Transactions on Signal Processing. 60, 7, p. 3449-3459 11 p.

    Research output: Contribution to journalArticle

  8. Variational Learning in Graphical Models and Neural Networks

    Bishop, C., 1998, ICANN 98: Proceedings of the 8th International Conference on Artificial Neural Networks, Skövde, Sweden, 2–4 September 1998. Niklasson, L., Boden, M. & Ziemke, T. (eds.). Springer London, p. 13-22 10 p. (Perspectives in Neural Computing).

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

  9. Variational Message Passing

    Winn, J. & Bishop, C., 2005, In : Journal of Machine Learning Research. 6, p. 661-694 34 p.

    Research output: Contribution to journalArticle

  10. Variational Noise-Contrastive Estimation

    Rhodes, B. & Gutmann, M., 25 Apr 2019, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019). Naha, Okinawa, Japan: PMLR, Vol. 89. p. 2741-2750 14 p.

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