Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Conference contribution › Research
  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. Variational Principal Components

    Bishop, C. M., 1 Jan 1999, Proceedings Ninth International Conference on Artificial Neural Networks, ICANN'99. IEE, p. 509-514 6 p.

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

  9. Variational Relevance Vector Machines

    Bishop, C. M. & Tipping, M. E., 1 Jan 2000, Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, p. 46-53 8 p.

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

  10. Variational inference for Markov jump processes

    Opper, M. & Sanguinetti, G., 2008, Advances in Neural Information Processing Systems 20 (NIPS 2007). Platt, J. C., Koller, D., Singer, Y. & Roweis, S. T. (eds.). p. 1105-1112 8 p.

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

  11. Verifying Anti-Security Policies Learnt from Android Malware Families

    Chen, W., Sutton, C., Aspinall, D., Gordon, A., Shen, Q. & Muttik, I., 21 Oct 2015, Fourth International Seminar on Program Verification, Automated Debugging and Symbolic Computation. 6 p.

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