Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Conference contribution › Research
  2. U-check: Model Checking and Parameter Synthesis under Uncertainty

    Bortolussi, L., Milios, D. & Sanguinetti, G., 2015, Quantitative Evaluation of Systems: 12th International Conference, QEST 2015, Madrid, Spain, September 1-3, 2015, Proceedings. Springer International Publishing, p. 89-104 16 p. (Lecture Notes in Computer Science; vol. 9259).

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

  3. Unsupervised deduplication using cross-field dependencies

    Hall, R., Sutton, C. & McCallum, A., 2008, Proceedings of the 14th ACM SIGKDD international conference on Knowledge Discovery and Data mining (KDD '08). New York, NY, USA: ACM, p. 310-317 8 p.

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

  4. Using Bayesian neural networks to classify segmented images

    Vivarelli, F. & Williams, C. K. I., 1 Jul 1997, Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440). IET, p. 268-273 6 p.

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

  5. Using Machine Learning to Focus Iterative Optimization

    Agakov, F., Bonilla, E., Cavazos, J., Franke, B., Fursin, G., O'Boyle, M. F. P., Thomson, J., Toussaint, M. & Williams, C. K. I., 2006, Proceedings of the International Symposium on Code Generation and Optimization. Washington, DC, USA: Institute of Electrical and Electronics Engineers (IEEE), p. 295-305 11 p. (CGO '06).

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

  6. Using a neural net to instantiate a deformable model

    Williams, C. K. I., Revow, M. & Hinton, G. E., 1995, Advances in neural information processing systems. p. 965-972 8 p.

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

  7. Using the Nyström Method to Speed Up Kernel Machines

    Williams, C. K. I. & Seeger, M., 2001, Advances in Neural Information Processing Systems 13 (NIPS 2000). Leen, T. K., Dietterich, T. G. & Tresp, V. (eds.). MIT Press, p. 682-688 7 p.

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

  8. 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

  9. 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

  10. 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

  11. 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