Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. A comparison of coordination and its variability in lower extremity segments during treadmill and overground running at different speeds

    Abbasi, A., Yazdanbakhsh, F., Tazji, M. K., Aghaie Ataabadi, P., Svoboda, Z., Nazarpour, K. & Vieira, M. F., 1 Jun 2020, In : Gait & Posture. 79, p. 139-144 6 p.

    Research output: Contribution to journalArticle

  2. Incoherent dictionary pair learning: application to a novel open-source database of Chinese numbers

    Abolghasemi, V., Chen, M., Alameer, A., Ferdowsi, S., Chambers, J. & Nazarpour, K., 1 Apr 2018, In : IEEE Signal Processing Letters. 25, 4, p. 472 - 476 5 p.

    Research output: Contribution to journalArticle

  3. Systematic biases in early ERP and ERF components as a result of high-pass filtering

    Acunzo, D., MacKenzie, G. & van Rossum, M. C. W., 2012, In : Journal of Neuroscience Methods. 209, 1, p. 212-218 7 p.

    Research output: Contribution to journalArticle

  4. The Gaussian process density sampler

    Adams, R. P., Murray, I. & MacKay, D. J. C., 2009, Advances in Neural Information Processing Systems 21: 22nd Annual Conference on Neural Information Processing Systems 2008. Koller, D. (ed.). Curran Associates Inc, p. 9-16 8 p.

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

  5. Comparing Mean Field and Exact EM in Tree Structured Belief Networks

    Adams, N. J., Williams, C. K. I. & Storkey, A. J., 2001, In Fourth International ICSC Symposium on Soft Computing and Intelligent Systems for Industry. ICSC-NAISO Adademic. Thoemmes Press, 6 p.

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

  6. MFDTs: Mean field dynamic trees

    Adams, NJ., Storkey, AJ., Ghahramani, Z. & Williams, CKI., 2000, 15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, PROCEEDINGS. Sanfeliu, A., Villanueva, JJ., Vanrell, M., Alquezar, R., Huang, T. & Serra, J. (eds.). LOS ALAMITOS: Institute of Electrical and Electronics Engineers (IEEE), p. 147-150 4 p. (INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION).

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

  7. Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

    Adams, R. P., Murray, I. & MacKay, D. J. C., 2009, Proceedings of the 26th International Conference on Machine Learning. 8 p.

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

  8. Dynamic Trees: Learning to Model Outdoor Scenes

    Adams, N. J. & Williams, C. K. I., 2002, Computer Vision — ECCV 2002: 7th European Conference on Computer Vision Copenhagen, Denmark, May 28–31, 2002 Proceedings, Part IV. Springer Berlin Heidelberg, p. 82-96 15 p. (Lecture Notes in Computer Science; vol. 2353).

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

  9. Dynamic trees for image modelling

    Adams, N. J. & Williams, C. K. I., Sep 2003, In : Image and vision computing. 21, 10, p. 865-877 13 p.

    Research output: Contribution to journalArticle

  10. Incorporating side information into probabilistic matrix factorization using Gaussian Processes

    Adams, R. P., Dahl, G. E. & Murray, I., 2010, Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010). 9 p.

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

  11. SDTs: sparse dynamic trees

    Adams, N. J. & Williams, C. K. I., 1999, Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470). IET, p. 527-532 vol.2 6 p.

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

  12. Real-time physiological tremor estimation using recursive singular spectrum analysis

    Adhikari, K., Tatinati, S., Veluvolu, K. C., Chambers, J. A. & Nazarpour, K., 14 Sep 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Institute of Electrical and Electronics Engineers (IEEE), p. 3202-3205 4 p.

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

  13. A Quaternion Weighted Fourier Linear Combiner for Modeling Physiological Tremor

    Adhikari, K., Tatinati, S., Ang, W. T., Veluvolu, K. C. & Nazarpour, K., 15 Feb 2016, In : IEEE Transactions on Biomedical Engineering. 63, 11, p. 2336 - 2346

    Research output: Contribution to journalArticle

  14. Modeling 3D tremor signals with a quaternion weighted Fourier Linear Combiner

    Adhikari, K., Tatinati, S., Veluvolu, K. C. & Nazarpour, K., 2 Jul 2015, 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER). Institute of Electrical and Electronics Engineers (IEEE), p. 799-802 4 p.

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

  15. Improvement in modelling of physiological tremor by inclusion of grip force in quaternion weighted Fourier linear combiner

    Adhikari, K., Tatinati, S., Veluvolu, K. C. & Nazarpour, K., 2 Dec 2015, 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP). IET, p. 1-5 5 p.

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

  16. Sparse instrumental variables (SPIV) for genome-wide studies

    Agakov, F. V., McKeigue, P., Krohn, J. & Storkey, A., 1 Jan 2010, Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  17. Discriminative Mixtures of Sparse Latent Fields for Risk Management

    Agakov, F. V., Orchard, P. & Storkey, A. J., 2012, Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12). Lawrence, N. D. & Girolami, M. A. (eds.). Vol. 22. p. 10-18 9 p.

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

  18. 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

  19. Object recognition with an elastic net-regularized hierarchical MAX model of the visual cortex

    Alameer, A., Ghazaei, G., Degenaar, P., Chambers, J. A. & Nazarpour, K., 1 Aug 2016, In : IEEE Signal Processing Letters. 23, 8, p. 1062 - 1066 5 p.

    Research output: Contribution to journalArticle

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