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

  20. Biologically-inspired object recognition system for recognizing natural scene categories

    Alameer, A., Degenaar, P. & Nazarpour, K., 9 Jan 2017, 2016 International Conference for Students on Applied Engineering (ICSAE). Institute of Electrical and Electronics Engineers (IEEE), p. 129-132 4 p.

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

  21. An elastic net-regularized HMAX model of visual processing

    Alameer, A., Ghazaei, G., Degenaar, P. & Nazarpour, K., 2 Dec 2015, 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP). IET, p. 1-4 4 p.

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

  22. Objects and scenes classification with selective use of central and peripheral image content

    Alameer, A., Degenaar, P. & Nazarpour, K., 9 Jan 2020, In : Journal of visual communication and image representation. 66, 11 p., 102698.

    Research output: Contribution to journalArticle

  23. Context-Based Object Recognition: Indoor Versus Outdoor Environments

    Alameer, A., Degenaar, P. & Nazarpour, K., 24 Apr 2019, Advances in Computer Vision. Arai, K. & Kapoor, S. (eds.). Cham: Springer International Publishing AG, p. 473-490 18 p. (Advances in Intelligent Systems and Computing; vol. 944).

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

  24. Processing occlusions using elastic-net hierarchical MAX model of the visual cortex

    Alameer, A., Degenaar, P. & Nazarpour, K., 8 Aug 2017, 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA). Institute of Electrical and Electronics Engineers (IEEE), p. 163-167 5 p.

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

  25. Synaptic interactome mining reveals p140Cap as a new hub for PSD proteins involved in psychiatric and neurological disorders

    Alfieri, A., Sorokina, O., Adrait, A., Angelini, C., Russo, I., Morellato, A., Matteoli, M., Menna, E., Erba, E. B., McLean, C., Armstrong, J. D., Ala, U., Buxbaum, J. D., Brusco, A., Couté, Y., De Rubeis, S., Turco, E. & Defilippi, P., 30 Jun 2017, In : Frontiers in Molecular Neuroscience. 10, p. 1-15 15 p., 212.

    Research output: Contribution to journalArticle

  26. Effects of ambient luminance on retinal information coding

    Alizadeh, A., Onken, A., Mutter, M., Münch, T. & Panzeri, S., 14 Sep 2017. 2 p.

    Research output: Contribution to conferenceAbstract

  27. Mining Idioms from Source Code

    Allamanis, M. & Sutton, C., 2014, Proceedings of the 22Nd ACM SIGSOFT International Symposium on Foundations of Software Engineering. New York, NY, USA: ACM, p. 472-483 12 p.

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

  28. A Survey of Machine Learning for Big Code and Naturalness

    Allamanis, M., Barr, E. T., Devanbu, P. & Sutton, C., 31 Jul 2018, In : ACM Computing Surveys. 51, 4, 36 p., 81.

    Research output: Contribution to journalArticle

  29. Learning Natural Coding Conventions

    Allamanis, M., Barr, E. T., Bird, C. & Sutton, C., 11 Nov 2014, Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering. New York, NY, USA: ACM, p. 281-293 13 p.

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

  30. Why, when, and what: Analyzing Stack Overflow questions by topic, type, and code

    Allamanis, M. & Sutton, C., 2013, Mining Software Repositories (MSR), 2013 10th IEEE Working Conference on. Institute of Electrical and Electronics Engineers (IEEE), p. 53-56 4 p.

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

  31. Learning Continuous Semantic Representations of Symbolic Expressions

    Allamanis, M., Chanthirasegaran, P., Kohli, P. & Sutton, C., 11 Aug 2017, The 34th International Conference on Machine Learning (ICML 2017). Sydney, Australia: PMLR, Vol. 70. p. 80-88 9 p. (Proceedings of Machine Learning Research; vol. 70).

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

  32. Suggesting Accurate Method and Class Names

    Allamanis, M., Barr, E. T., Bird, C. & Sutton, C., 2015, Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering. ACM, p. 38-49 12 p.

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

  33. Mining source code repositories at massive scale using language modeling

    Allamanis, M. & Sutton, C., 2013, Mining Software Repositories (MSR), 2013 10th IEEE Working Conference on. Institute of Electrical and Electronics Engineers (IEEE), p. 207-216 10 p.

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

  34. Mining Semantic Loop Idioms

    Allamanis, M., Barr, E. T., Bird, C., Devanbu, P., Marron, M. & Sutton, C., 1 Jul 2018, In : IEEE Transactions on Software Engineering. 44, 7, 18 p.

    Research output: Contribution to journalArticle

  35. A Convolutional Attention Network for Extreme Summarization of Source Code

    Allamanis, M., Peng, H. & Sutton, C., 24 Jun 2016, Proceedings of The 33rd International Conference on Machine Learning, PMLR. New York, United States: PMLR, Vol. 48. p. 2091-2100 10 p. (Proceedings of Machine Learning Research; vol. 48).

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

  36. Object localisation using the Generative Template of Features

    Allan, M. & Williams, C. K. I., Jul 2009, In : Computer Vision and Image Understanding. 113, 7, p. 824-838 15 p.

    Research output: Contribution to journalArticle

  37. Harmonising chorales by probabilistic inference

    Allan, M. & Williams, C. K. I., 2005, Advances in Neural Information Processing Systems 17 (NIPS 2004). MIT Press, p. 25-32 8 p.

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

  38. Fast Learning of Sprites using Invariant

    Allan, M., Titsias, M. K. & Williams, C. K. I., 2005, Proceedings of the British Machine Vision Conference 2005. BMVA Press, 10 p.

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

  39. The Effects of Ectopic White and Transformer Expression on Drosophila Courtship Behavior

    An, X., Armstrong, J. D., Kaiser, K. & O'Dell, K. M. C., 2000, In : Journal of neurogenetics. 14, 4, p. 227-243 17 p.

    Research output: Contribution to journalArticle

  40. Reconciling models of V1 development and adult function

    Antolik, J., Law, J. S. & Bednar, J., 2009.

    Research output: Contribution to conferencePoster

  41. Modeling the development of maps of complex cells in V1

    Antolik, J. & Bednar, J., 2007.

    Research output: Contribution to conferenceAbstract

  42. Development of maps of simple and complex cells in the primary visual cortex

    Antolik, J. & Bednar, J. A., 2011, In : Frontiers in Computational Neuroscience. 5, 17

    Research output: Contribution to journalArticle

  43. Developing maps of complex cells in a computational model

    Antolik, J. & Bednar, J., 13 Jul 2008.

    Research output: Contribution to conferencePoster

  44. Modelling surround modulation in the LGN

    Antolik, J. & Bednar, J., Nov 2007.

    Research output: Contribution to conferencePoster

  45. Developing maps of complex cells in a computational model of V1

    Antolik, J. & Bednar, J., 19 Nov 2008.

    Research output: Contribution to conferencePoster

  46. Design of the TRONCO BioConductor Package for TRanslational ONCOlogy

    Antoniotti, M., Caravagna, G., De Sano, L., Graudenzi, A., Mauri, G., Mishra, B. & Ramazzotti, D., 21 Oct 2016, In : The R Journal. 8, 2, p. 39-59 21 p.

    Research output: Contribution to journalArticle

  47. How to train your MAML

    Antoniou, A., Edwards, H. & Storkey, A., 2019. 11 p.

    Research output: Contribution to conferencePaper

  48. Augmenting Image Classifiers Using Data Augmentation Generative Adversarial Networks

    Antoniou, A., Storkey, A. & Edwards, H., 27 Sep 2018, Artificial Neural Networks and Machine Learning – ICANN 2018. Rhodes, Greece, p. 594-603 10 p. (Lecture Notes in Computer Science; vol. 11141).

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

  49. Sex, Flies and no Videotape

    Armstrong, D., Baker, D. A., Heward, J. A. & Lukins, T. C., 2005, 5th International Conference on Methods and Techniques in Behavioral Research.

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

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