Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Conference contribution › Research
  2. Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation

    Zhong, M., Goddard, N. & Sutton, C., 2014, Advances in Neural Information Processing Systems 27 (NIPS 2014). Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D. & Weinberger, K. Q. (eds.). Palais des Congrès de Montréal, Montréal, CANADA : Curran Associates Inc, p. 3590-3598 9 p.

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

  3. Signal masking in Gaussian channels

    Quinn, J. A. & Williams, C. K. I., 2008, Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on. p. 2989-2992 4 p.

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

  4. Slice sampling covariance hyperparameters of latent Gaussian models

    Murray, I. & Adams, R. P., 2010, Neural Information Processing Systems Conference 2010. 9 p.

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

  5. Sparse Forward-Backward Using Minimum Divergence Beams for Fast Training Of Conditional Random Fields

    Pal, C., Sutton, C. & McCallum, A., 1 May 2006, Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on. Institute of Electrical and Electronics Engineers (IEEE), Vol. 5. p. 581-584 4 p.

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

  6. Statistical Machine Learning Makes Automatic Control Practical for Internet Datacenters

    Bodík, P., Griffith, R., Sutton, C., Fox, A., Jordan, M. & Patterson, D., 2009, Proceedings of the 2009 Conference on Hot Topics in Cloud Computing (HotCloud 2009). Berkeley, CA, USA: USENIX Association, 5 p.

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

  7. Statistical abstraction for multi-scale spatio-temporal systems

    Michaelides, M., Hillston, J. & Sanguinetti, G., 11 Aug 2017, International Conference on Quantitative Evaluation of Systems QEST 2017: Quantitative Evaluation of Systems . Springer, Cham, p. 243-258 16 p. (Lecture Notes in Computer Science; vol. 10503).

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

  8. Statistical models of images and early vision

    Hyvärinen, A., Hoyer, P. O., Hurri, J. & Gutmann, M., 2005, Proc. of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR). p. 1-14 14 p.

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

  9. Stochastic Parallel Block Coordinate Descent for Large-scale Saddle Point Problems

    Zhu, Z. & Storkey, A. J., Feb 2016, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI Press, p. 2429-2534 106 p. (AAAI'16).

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

  10. Stochastic simulation of enzymatic reactions under transcriptional feedback regulation

    Lugagne, J., Oyarzún, D. A. & Stan, G. V., 1 Jul 2013, 2013 European Control Conference (ECC). p. 3646-3651 6 p.

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

  11. Structural Expectation Propagation (SEP): Bayesian Structure Learning for Networks with Latent Variables

    Lazic, N., Bishop, C. M. & Winn, J., 1 May 2013, Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics (AIStats), Scottsdale, AZ, USA. Carvalho, C. M. & Ravikumar, P. (eds.). Journal of Machine Learning Research: Workshop and Conference Proceedings, Vol. 31. p. 379-387 9 p.

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