Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Double objective optimal multivariable ripple-free deadbeat control

    Salgado, M. E. & Oyarzún, D. A., 2007, In : International journal of control. 80, 5, p. 763-773 11 p.

    Research output: Contribution to journalArticle

  2. Drosophila circadian rhythms in semi-natural environments; the summer afternoon component is not an artifact and requires TrpA1 channels

    Green, E. W., O'Callaghan, E. K., Hansen, C. N., Bastianello, S., Bhutani, S., Vanin, S., Armstrong, J. D., Costa, R. & Kyriacou, C. P., 2015, In : Proceedings of the National Academy of Sciences. 112, 28, p. 8702-8707 6 p.

    Research output: Contribution to journalArticle

  3. Drosophila melanogaster: The model organism of choice for the complex biology of multicellular organisms

    Beckingham, K. M., Armstrong, D., Texada, M. J., Munjaal, R. & Baker, D. A., 2005, In : Gravitational and Space Biology. 18, 2

    Research output: Contribution to journalArticle

  4. Dying mRNA Tells a Story of Its Life

    Wallace, EW. J. & Drummond, D. A., 4 Jun 2015, In : Cell. 161, 6, p. 1246 - 1248 3 p.

    Research output: Contribution to journalArticle

  5. Dynamic Conditional Random Fields for Jointly Labeling Multiple Sequences

    McCallum, A., Rohanimanesh, K. & Sutton, C., 2003, NIPS Workshop on Syntax, Semantics, and Statistics. 8 p.

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

  6. Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data

    Sutton, C., Rohanimanesh, K. & McCallum, A., 2004, Proceedings of the Twenty-first International Conference on Machine Learning. New York, NY, USA: ACM, 8 p.

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

  7. Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data

    Sutton, C., McCallum, A. & Rohanimanesh, K., 1 May 2007, In : Journal of Machine Learning Research. 8, p. 693-723 31 p.

    Research output: Contribution to journalArticle

  8. Dynamic Evaluation of Neural Sequence Models

    Krause, B., Mbabazi, E., Murray, I. & Renals, S., 1 Oct 2018, Proceedings of the 35th International Conference on Machine Learning. Dy, J. & Krause, A. (eds.). Stockholmsmässan, Stockholm Sweden: PMLR, Vol. 80. p. 2766-2775 10 p. (Proceedings of Machine Learning Research).

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

  9. Dynamic Evaluation of Transformer Language Models

    Krause, B., Mbabazi, E., Murray, I. & Renals, S., 17 Apr 2019, 6 p.

    Research output: Working paper

  10. Dynamic Positional Trees for Structural Image Analysis

    Storkey, A. & Williams, C. K. I., 2001, In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics. p. 298-304 7 p.

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

  11. Dynamic Trees: A Structured Variational Method Giving Efficient Propagation Rules

    J. Storkey, A., 16 Jan 2000, UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence. Morgan Kaufmann, p. 566-573 8 p.

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

  12. 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

  13. Dynamic competition between contour integration and contour segmentation probed with moving stimuli

    Lorenceau, J., Giersch, A. & Seriés, P., 1 Jan 2005, In : Vision Research. 45, 1, p. 103-116

    Research output: Contribution to journalArticle

  14. Dynamic metabolic control: towards precision engineering of metabolism

    Liu, D., Mannan, A. A., Han, Y., Oyarzun, D. & Zhang, F., 1 Jul 2018, In : Journal of Industrial Microbiology & Biotechnology. 45, 7, p. 535-543 9 p.

    Research output: Contribution to journalArticle

  15. Dynamic optimization of metabolic networks coupled with gene expression

    Waldherr, S., Oyarzún, D. A. & Bockmayr, A., 21 Jan 2015, In : Journal of Theoretical Biology. 365, p. 469-485 17 p.

    Research output: Contribution to journalArticle

  16. Dynamic structure super-resolution

    Storkey, A. J., 2002, Advances in Neural Information Processing Systems 15 (NIPS 2002). MIT Press, p. 1295-1302 8 p.

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

  17. 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

  18. Dynamical Constraints on Using Precise Spike Timing to Compute in Recurrent Cortical Networks

    Banerjee, A., Seriés, P. & Pouget, A., 1 Apr 2008, In : Neural Computation. 20, 4, p. 974-993

    Research output: Contribution to journalArticle

  19. Dynamical Inference from a Kinematic Snapshot: The Force Law in the Solar System

    Bovy, J., Murray, I. & Hogg, D. W., Mar 2010, In : Astrophysical Journal. 711, 2, p. 1157-1167 11 p.

    Research output: Contribution to journalArticle

  20. Dynamics and Robustness of Familiarity Memory

    Cortes, J. M., Greve, A., Barrett, A. B. & van Rossum, M. C. W., 20 Oct 2009, In : Neural Computation. 22, 2, p. 448-466 19 p.

    Research output: Contribution to journalArticle

  21. Dynamics of Elongation Factor 2 Kinase Regulation in Cortical Neurons in Response to Synaptic Activity

    Kenney, J. W., Sorokina, O., Genheden, M., Sorokin, A., Armstrong, J. D. & Proud, C. G., 18 Feb 2015, In : Journal of Neuroscience. 35, 7, p. 3034-3047 14 p.

    Research output: Contribution to journalArticle

  22. Dynamics of a starvation-to-surfeit shift: a transcriptomic and modelling analysis of the bacterial response to zinc reveals transient behaviour of the Fur and SoxS regulators

    Graham, A. I., Sanguinetti, G., Bramall, N., McLeod, C. W. & Poole, R. K., 2012, In : Microbiology. 158, 1, p. 284-292 9 p.

    Research output: Contribution to journalArticle

  23. Dynamics of complex feedback architectures in metabolic pathways

    Chaves, M. & Oyarzun, D., 1 Jan 2019, In : Automatica. 99, p. 323-332 10 p.

    Research output: Contribution to journalArticle

  24. ELFI: Engine for Likelihood-Free Inference

    Lintusaari, J., Vuollekoski, H., Kangasrääsiö, A., Skyten, K., Järvenpää, M., Marttinen, P., Gutmann, M., Vehtari, A., Corander, J. & Kaski, S., 20 Jun 2018, (Accepted/In press) In : Journal of Machine Learning Research. 8 p.

    Research output: Contribution to journalArticle

  25. EM optimization of latent-variable density models

    Bishop, C. M., Svensén, M. & Williams, C. KI., 1996, Advances in Neural Information Processing Systems 8 (NIPS 1995). MIT Press, p. 465-471 7 p.

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

  26. Early-Stage Waves in the Retinal Network Emerge Close to a Critical State Transition between Local and Global Functional Connectivity

    Hennig, M. H., Adams, C., Willshaw, D. & Sernagor, E., Jan 2009, In : The Journal of Neuroscience. 29, 4, p. 1077-1086 10 p.

    Research output: Contribution to journalArticle

  27. Edge co-occurrences are sufficient to categorize natural versus animal images

    Perrinet, L. U. & Bednar, J. A., 22 Aug 2014, In : Journal of Vision. 14, 10, p. 1310-1310

    Research output: Contribution to journalArticle

  28. Editorial: Special Issue on Probabilistic Models for Image Understanding

    Triggs, B. & Williams, C. K. I., 10 Jun 2010, In : International Journal of Computer Vision. 88, 2, p. 145-146 2 p.

    Research output: Contribution to journalEditorial

  29. Effect of downstream feedback on the achievable performance of feedback control loops for serial processes

    Oyarzun, D. & Silva, E., 2007, 2007 European Control Conference, ECC 2007. p. 1727-1733 7 p.

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

  30. Effects of Noise on the Spike Timing Precision of Retinal Ganglion Cells

    van Rossum, M. C. W., O'Brien, B. J. & Smith, R. G., 2003, In : Journal of Neurophysiology. 89, 5, p. 2406-2419 14 p.

    Research output: Contribution to journalArticle

  31. 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

  32. Effects of fixational eye movements on retinal ganglion cell responses: a modelling study

    Hennig, M. H. & Wörgötter, F., 2007, In : Frontiers in Computational Neuroscience. 1, 2, p. 69-84

    Research output: Contribution to journalArticle

  33. Efficient Bayesian Experimental Design for Implicit Models

    Kleinegesse, S. & 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. 476-485 10 p.

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

  34. Efficient acquisition rules for model-based approximate Bayesian computation

    Järvenpää, M., Gutmann, M., Pleska, A., Vehtari, A. & Marttinen, P., 18 Sep 2018, In : Bayesian analysis. 28 p.

    Research output: Contribution to journalArticle

  35. Efficient low-order approximation of first-passage time distributions

    Schnoerr, D., Cseke, B., Grima, R. & Sanguinetti, G., 20 Nov 2017, In : Physical Review Letters. 5 p., 210601 .

    Research output: Contribution to journalArticle

  36. Efficient stochastic simulation of systems with multiple time scales via statistical abstraction

    Bortolussi, L., Milios, D. & Sanguinetti, G., 2015, Computational Methods in Systems Biology: 13th International Conference, CMSB 2015, Nantes, France, September 16-18, 2015, Proceedings. Springer International Publishing, p. 40-51 12 p. (Lecture Notes in Computer Science; vol. 9308).

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

  37. Elemental and non-elemental olfactory learning in Drosophila

    Young, J., Wessnitzer, J., Armstrong, D. & Webb, B., 2011, In : Neurobiology of Learning and Memory. 96, 2, p. 339-352 14 p.

    Research output: Contribution to journalArticle

  38. Elliptical slice sampling

    Murray, I., Adams, R. P. & MacKay, D. J. C., 2010, Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS). Journal of Machine Learning Research: Workshop and Conference Proceedings, p. 541-548 8 p. (Journal of Machine Learning Research: Workshop and Conference Proceedings; vol. 9).

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

  39. Elucidating Poor Decision-Making in a Rat Gambling Task

    Rivalan, M., Valton, V., Series, P., Marchand, A. R., Dellu-hagedorn, F. & Ravel, N. (ed.), 5 Dec 2013, In : PLoS ONE. 8, 12, p. e82052

    Research output: Contribution to journalArticle

  40. Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data

    Thorne, T., 10 Dec 2015, In : Statistical applications in genetics and molecular biology. 14, 6, p. 575-583 9 p.

    Research output: Contribution to journalArticle

  41. Empirical vine copula modeling to study multivariate neural representations during complex behaviors

    Safaai, H., Chettih, S., Onken, A., Panzeri, S. & Harvey, C., 3 Mar 2018, p. 244-245. 2 p.

    Research output: Contribution to conferenceAbstract

  42. Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics

    Tsai, Y-S., Perrotta, C. & Gasevic, D., 25 Nov 2019, In : Assessment & Evaluation in Higher Education. p. 1-14 23 p.

    Research output: Contribution to journalArticle

  43. Encoding words into a Potts attractor network

    Pirmoradian, S., Treves, A. & SISSA, C. N. S., 2013, Progress in Neural Processing: Proceedings of the 13th Neural Computation and Psychology Workshop . World Scientific Publishing, p. 29-42 14 p.

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

  44. Endoscopic sensing of distal lung physiology

    Choudhury, D., Tanner, M. G., McAughtrie, S., Yu, F., Mills, B., Choudhary, T. R., Seth, S., Craven, T. H., Stone, J. M., Mati, I. K., Campbell, C. J., Bradley, M., Williams, C. K. I., Dhaliwal, K., Birks, T. A. & Thomson, R. R., 18 Mar 2019, In : Journal of Physics: Conference Series. 1151, 9 p., 012009.

    Research output: Contribution to journalArticle

  45. Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules

    Sacramento, J., Wichert, A. & van Rossum, M. C. W., Jun 2015, In : PLoS Computational Biology. 11, 6, e1004265.

    Research output: Contribution to journalArticle

  46. Enhance: The Assembly Rooms Energy Living Lab

    Carter, C., Morgan, E., Webb, L., Goddard, N. & Webb, J., 31 Jul 2017, PLEA 2017 Conference Proceedings. Roaf, S. (ed.). Edinburgh: PLEA (Passive and Low Energy Architecture), Vol. 1. p. 669-676

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

  47. Erratum: Author Correction: Neurons and neuronal activity control gene expression in astrocytes to regulate their development and metabolism (Nature communications (2017) 8 (15132))

    Hasel, P., Dando, O., Jiwaji, Z., Baxter, P., Todd, A. C., Heron, S., Márkus, N. M., McQueen, J., Hampton, D. W., Torvell, M., Tiwari, S. S., McKay, S., Eraso-Pichot, A., Zorzano, A., Masgrau, R., Galea, E., Chandran, S., Wyllie, D. J. A., Simpson, T. I. & Hardingham, G. E., 6 Feb 2018, In : Nature Communications. 9, 1 p.

    Research output: Contribution to journalComment/debate

  48. Error estimates and specification parameters for functional renormalization

    Schnoerr, D., Boettcher, I., Pawlowski, J. M. & Wetterich, C., Jul 2013, In : Annals of physics. 334, p. 83 - 99 17 p.

    Research output: Contribution to journalArticle

  49. Establishing an Appropriate Learning Bias Through Development

    Valsalam, V. K., Bednar, J. A. & Miikkulainen, R., 2006, Proceedings of the Fifth International Conference on Development and Learning (ICDL-2006).

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

  50. Estimating Bacterial Load in FCFM Imaging

    Seth, S., Akram, A., Dhaliwal, K. & Williams, C. K. I., 22 Jun 2017, Medical Image Understanding and Analysis. MIUA 2017.. Springer, Cham, p. 909-921 12 p. (Communications in Computer and Information Science; vol. 723).

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

  51. Estimating Bacterial and Cellular Load in FCFM Imaging

    Seth, S., Akram, A., Dhaliwal, K. & Williams, C. K. I., 5 Jan 2018, In : Journal of Imaging . 4, 1, 17 p.

    Research output: Contribution to journalArticle

  52. Estimating Dependency Structures for non-Gaussian Components with Linear and Energy Correlations

    Hiroaki, S., Gutmann, M. U., Shouno, H. & Hyärinen, A., 2014, Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR: WCP volume 33. Journal of Machine Learning Research - Proceedings Track, p. 868-876 9 p.

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

  53. Estimating the fraction of falsely detected spikes in high density microelectrode array recordings based on correlations

    Muthmann, J., Amin, H., Maccione, A., Sernagor, E., Berdondini, L., Hennig, M. H. & Bhalla, U. S., 1 Jan 2013, In : BMC Neuroscience. 14, Suppl 1, p. 1-2 2 p.

    Research output: Contribution to journalArticle

  54. Estimating the location and size of retinal injections from orthogonal images of an intact retina

    Hjorth, J. J. J., Savier, E., Sterratt, D. C., Reber, M. & Eglen, S. J., 21 Nov 2015, In : BMC Neuroscience. 16, 1, p. 1-10 10 p.

    Research output: Contribution to journalArticle

  55. Estimating the size of the human interactome

    Stumpf, M. P. H., Thorne, T., De Silva, E., Stewart, R., Hyeong, J. A., Lappe, M. & Wiuf, C., 13 May 2008, In : Proceedings of the National Academy of Sciences. 105, 19, p. 6959-6964 6 p.

    Research output: Contribution to journalArticle

  56. Estimation Bias in Maximum Entropy Models

    Macke, J. H., Murray, I. & Latham, P. E., Aug 2013, In : Entropy. 15, 8, p. 3109-3129 21 p.

    Research output: Contribution to journalArticle

  57. Estimation of unnormalized statistical models without numerical integration

    Gutmann, M. & Hyvärinen, A., 2013, Proc. Workshop on Information Theoretic Methods in Science and Engineering (WITMSE2013). 8 p.

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

  58. Evaluating probabilities under high-dimensional latent variable models

    Murray, I. & Salakhutdinov, R., 2009, Advances in Neural Information Processing Systems 21. Koller, D., Schuurmans, D., Bengio, Y. & Bottou, L. (eds.). p. 1137-1144 8 p.

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

  59. Evaluation methods for topic models

    Wallach, H. M., Murray, I., Salakhutdinov, R. & Mimno, D., 2009, Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09). New York, NY, USA: ACM, p. 1105-1112 8 p.

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

  60. Evaluation of a pre-surgical functional MRI workflow: From data acquisition to reporting

    Pernet, C., Krzysztof, G., Job, D., Rodriguez Gonzalez, D., Storkey, A., Whittle, I. & Wardlaw, J., Feb 2016, In : International journal of medical informatics. 86, p. 37-42 6 p.

    Research output: Contribution to journalArticle

  61. Event-related fMRI of word classification and successful word recognition in subjects at genetically enhanced risk of schizophrenia

    Whyte, M-C., Whalley, H. C., Simonotto, E., Flett, S., Shillcock, R., Marshall, I., Goddard, N. H., Johnstone, E. C. & Lawrie, S. M., Oct 2006, In : Psychological Medicine . 36, 10, p. 1427-39 13 p.

    Research output: Contribution to journalArticle

  62. Evidence for evolutionary divergence of activity-dependent gene expression in developing neurons

    Qiu, J., McQueen, J., Bilican, B., Dando, O., Magnani, D., Punovuori, K., Selvaraj, B. T., Livesey, M., Haghi, G., Heron, S., Burr, K., Patani, R., Rajan, R., Sheppard, O., Kind, P. C., Simpson, I., Tybulewicz, V. LJ., Wyllie, D. JA., Fisher, E. MC., Lowell, S. & 3 others, Chandran, S., Hardingham, G. E. & West, A. (ed.), 3 Nov 2016, In : eLIFE. 5, 21 p., e20337.

    Research output: Contribution to journalArticle

  63. Evolution at the system level: the natural history of protein interaction networks

    Stumpf, M. P. H., Kelly, W. P., Thorne, T. & Wiuf, C., 1 Jul 2007, In : Trends in Ecology & Evolution. 22, 7, p. 366-373 8 p.

    Research output: Contribution to journalArticle

  64. Evolution of the Cognitive Proteome: From Static to Dynamic Network Models

    Armstrong, J. D. & Sorokina, O., 2012, Advances in Systems Biology. Goryanin, I. I. & Goryachev, A. B. (eds.). Springer New York, p. 119-134 16 p. (Advances in Experimental Medicine and Biology; vol. 736).

    Research output: Chapter in Book/Report/Conference proceedingChapter

  65. Evolution of the synapse proteome

    Armstrong, J. D., Malik, B., Pocklington, A., Emes, R. & Grant, S., 2009, In : Journal of neurogenetics. 23, p. S34-S35 2 p.

    Research output: Contribution to journalMeeting abstract

  66. Evolutionary dynamics of residual disease in human glioblastoma

    Spiteri, I., Caravagna, G., Cresswell, G., Vatsiou, A., Nichol, D., Acar, A., Ermini, L., Chkhaidze, K., Werner, B., Mair, R., Brognaro, E., Verhaak, R., Sanguinetti, G., Sara, P., Watts, C. & Sottoriva, A., Mar 2019, In : Annals of oncology. 30, 3, p. 456–463 8 p., mdy506.

    Research output: Contribution to journalArticle

  67. Evolutionary expansion and anatomical specialization of synapse proteome complexity

    Emes, R. D., Pocklington, A. J., Anderson, C. N. G., Bayes, A., Collins, M. O., Vickers, C. A., Croning, M. D. R., Malik, B. R., Choudhary, J. S., Armstrong, D. & Grant, S., Jul 2008, In : Nature Neuroscience. 11, 7, p. 799-806 8 p.

    Research output: Contribution to journalArticle

  68. Evolving plastic responses in artificial cell models

    Maher, J., Morgan, F. & Mac Aodha, O., 29 May 2009, 2009 IEEE Congress on Evolutionary Computation. Institute of Electrical and Electronics Engineers (IEEE), p. 3018-3023 6 p.

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

  69. Exact Calculation of the Hessian Matrix for the Multilayer Perceptron

    Bishop, C., 1 Jul 1992, In : Neural Computation. 4, 4, p. 494-501 8 p.

    Research output: Contribution to journalArticle

  70. Excitability changes that complement Hebbian learning.

    Janowitz, M. K. & van Rossum, M. C. W., Mar 2006, In : Network: Computation in Neural Systems. 17, 1, p. 31 - 41

    Research output: Contribution to journalArticle

  71. Expectation propagation for continuous time stochastic processes

    Cseke, B., Schnoerr, D., Opper, M. & Sanguinetti, G., 14 Nov 2016, In : Journal of Physics A: Mathematical and Theoretical. 49, 18 p., 494002.

    Research output: Contribution to journalArticle

  72. Expectation-Maximization Methods for Solving (PO)MDPs and Optimal Control Problems.

    Toussaint, M., Storkey, A. J. & Harmeling, S., 2011, Bayesian Time Series Models. Chiappa, S. & Barber, D. (eds.). Cambridge University Press, p. 388-413 26 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  73. Expectations developed over multiple timescales facilitate visual search performance

    Gekas, N., Seitz, A. R. & Seriés, P., Jul 2015, In : Journal of Vision. 15, 9, 10.

    Research output: Contribution to journalArticle

  74. Experimental design for inference over the A. thaliana circadian clock network

    Trejo-Banos, D., Millar, A. J. & Sanguinetti, G., 2015, Computational Methods in Systems Biology: 13th International Conference, CMSB 2015, Nantes, France, September 16-18, 2015, Proceedings. Springer International Publishing, p. 28-39 12 p. (Lecture Notes in Computer Science; vol. 9308).

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

  75. Explaining Unwanted Behaviours in Context

    Chen, W., Aspinall, D., Gordon, A., Sutton, C. & Muttik, I., 6 Apr 2016, Proceedings of the 1st International Workshop on Innovations in Mobile Privacy and Security co-located with the International Symposium on Engineering Secure Software and Systems (ESSoS 2016). CEUR-WS.org, p. 38-45 8 p.

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

  76. Exploiting Machine Learning to Subvert Your Spam Filter

    Nelson, B., Barreno, M., Chi, F. J., Joseph, A. D., Rubinstein, B. I. P., Saini, U., Sutton, C., Tygar, J. D. & Xia, K., 2008, Proceedings of the 1st Usenix Workshop on Large-Scale Exploits and Emergent Threats. Berkeley, CA, USA: USENIX Association, p. 7:1-7:9 9 p.

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

  77. Exploration by random network distillation

    Burda, Y., Edwards, H., Storkey, A. & Klimov, O., 2019. 17 p.

    Research output: Contribution to conferencePaper

  78. Exploring the impact of model calibration on estimating energy savings through better space heating control

    Marini, D., Webb, L., Diamantis, G. & Buswell, R. A., 2014, Proceedings of Building Simulation and Optimization: Second Conference of IBPSA-England conference in association with CIBSE. International Building Performance Simulation Association, p. 1-8 8 p.

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

  79. Expression of mRNA Encoding Mcu and Other Mitochondrial Calcium Regulatory Genes Depends on Cell Type, Neuronal Subtype, and Ca2+ Signaling

    Work enabled by Edinburgh Genomics, 2 Feb 2016, In : PLoS ONE. 11, 2, 16 p., 0148164.

    Research output: Contribution to journalArticle

  80. Extensions to copula based modeling of spike counts

    Klaus, O., Onken, A. & Grünewälder, S., 3 Feb 2009. 1 p.

    Research output: Contribution to conferenceAbstract

  81. Extracting Motion Primitives from Natural Handwriting Data

    Williams, B. H., Toussaint, M. & Storkey, A. J., 2006, Artificial Neural Networks – ICANN 2006. Kollias, S., Stafylopatis, A., Duch, W. & Oja, E. (eds.). Springer-Verlag Berlin Heidelberg, p. 634-643 (Lecture Notes in Computer Science; vol. 4132).

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

  82. Extracting coactivated features from multiple data sets

    Gutmann, M. & Hyvärinen, A., 2011, Artificial Neural Networks and Machine Learning – ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I. Honkela, T. (ed.). Berlin, Heidelberg: Springer Berlin Heidelberg, p. 323-330 8 p. (Lecture Notes in Computer Science; vol. 6791).

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

  83. Extraction of synaptic input properties in vivo

    Puggioni, P., Jelitai, M., Duguid, I. & Van Rossum, M., 31 May 2017, In : Neural Computation. 26 p.

    Research output: Contribution to journalArticle

  84. Eye Micro-movements Improve Stimulus Detection Beyond the Nyquist Limit in the Peripheral Retina

    Hennig, M. H. & Wörgötter, F., 2004, Advances in Neural Information Processing Systems 16 (NIPS 2003). Thrun, S., Saul, L. K. & Schölkopf, B. (eds.). MIT Press, p. 1475-1482 8 p.

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

  85. FISSA: A neuropil decontamination toolbox for calcium imaging signals

    Keemink, S., Lowe, S., Pakan, J., Dylda, E., Van Rossum, M. & Rochefort, N., 22 Feb 2018, In : Nature Human Behaviour.

    Research output: Contribution to journalArticle

  86. Face Painting: querying art with photos

    Crowley, E., Parkhi, O. M. & Zisserman, A., 2015, British Machine Vision Conference, 2015.

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

  87. Factored Shapes and Appearances for Parts-based Object Understanding

    Eslami, S. M. & Williams, C. K. I., 2011, Proceedings of the British Machine Vision Conference. BMVA Press, p. 1-12 12 p. 18

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

  88. Factorial Switching Linear Dynamical Systems Applied to Physiological Condition Monitoring

    Quinn, J. A., Williams, C. K. I. & McIntosh, N., 2009, In : IEEE Transactions on Pattern Analysis and Machine Intelligence. 31, 9, p. 1537-1551 15 p.

    Research output: Contribution to journalArticle

  89. Factorial switching Kalman filters for condition monitoring in neonatal intensive care

    Williams, C. K. I., Quinn, J. & McIntosh, N., 2006, Advances in Neural Information Processing Systems 18. MIT Press, p. 1513-1520 8 p.

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

  90. 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

  91. Fast Propagation of Firing Rates through Layered Networks of Noisy Neurons

    van Rossum, M. C. W., Turrigiano, G. G. & Nelson, S. B., 2002, In : The Journal of Neuroscience. 22, 5, p. 1956-1966 11 p.

    Research output: Contribution to journalArticle

  92. Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation

    Papamakarios, G. & Murray, I., 10 Dec 2016, 30th Conference on Neural Information Processing Systems (NIPS 2016). Barcelona, Spain: Curran Associates Inc, p. 1028-1036 9 p.

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

  93. Fast, Piecewise Training for Discriminative Finite-state and Parsing Models

    Sutton, C. & McCallum, A., 2005, Center for Intelligent Information Retrieval, 8 p. (Center for Intelligent Information Retrieval Technical Reports; no. IR-403).

    Research output: Working paper

  94. Feedback Inhibition Enables Theta-Nested Gamma Oscillations and Grid Firing Fields

    Pastoll, H., Solanka, L., van Rossum, M. C. W. & Nolan, M. F., 9 Jan 2013, In : Neuron. 77, 1, p. 141-154 14 p.

    Research output: Contribution to journalArticle

  95. Female receptivity phenotype of icebox mutants caused by a mutation in the L1-type cell adhesion molecule neuroglian

    Carhan, A., Allen, F., Armstrong, J. D., Goodwin, S. F. & O'Dell, K. M. C., 2005, In : Genes Brain and Behavior. 4, 8, p. 449-465 17 p.

    Research output: Contribution to journalArticle

  96. Fisher and Shannon Information in Finite Neural Populations

    Yarrow, S., Challis, E. & Seriès, P., 19 Mar 2012, In : Neural Computation. 24, 7, p. 1740-1780 41 p.

    Research output: Contribution to journalArticle

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