Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Adaptation of anaerobic cultures of Escherichia coli K-12 in response to environmental trimethylamine-N-oxide

    Denby, K. J., Rolfe, M. D., Crick, E., Sanguinetti, G., Poole, R. K. & Green, J., Jul 2015, In : Environmental Microbiology. 17, 7, p. 2477-2491 15 p.

    Research output: Contribution to journalArticle

  2. Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster

    Dewar, M. A., Kadirkamanathan, V., Opper, M. & Sanguinetti, G., 10 Mar 2010, In : BMC Systems Biology. 4, 9 p., 21.

    Research output: Contribution to journalArticle

  3. Classification of Animal Behaviour Using Dynamic Models of Movement

    Dewar, M. A., Lukins, T. C., Heward, J. A. & Armstrong, D., 2008.

    Research output: Contribution to conferencePoster

  4. Capturing Data Uncertainty in High-Volume Stream Processing

    Diao, Y., Li, B., Liu, A., Peng, L., Sutton, C. A., Tran, T. T. L. & Zink, M., 2009, 4th Biennial Conference on Innovative Data Systems Research (CIDR). 11 p.

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

  5. Analysis of compound heterozygotes reveals that the mouse floxed Pax6 tm1Ued allele produces abnormal eye phenotypes

    Dorà, N. J., Crookshanks, A. J. F., Leung, K. K. Y., Simpson, T., Mason, J., Price, D. & West, J., 30 May 2016, In : Transgenic Research. p. 1-14 14 p.

    Research output: Contribution to journalArticle

  6. Selective RNA Sequestration Mediated by a Heat-Sensing Disordered Protein Region

    Drummond, D. A., Pilipenko, E., Riback, J., Scott, J., Rojek, A., Budnik, B., Wallace, E. & Katanski, C., Apr 2015, In : The FASEB Journal. 29, 1 Supplement

    Research output: Contribution to journalArticle

  7. Pseudo-marginal Markov Chain Monte Carlo for Nonnegative Matrix Factorization

    Du, J. & Zhong, M., Apr 2017, In : Neural Processing Letters. 45, 2, p. 553-562 10 p.

    Research output: Contribution to journalArticle

  8. Context Matters: Towards Extracting a Citation’s Context Using Linguistic Features

    Duma, D., Sutton, C. & Klein, E., 2016, JCDL '16 Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries. ACM, p. 201-202 2 p.

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

  9. The use of data-mining for the automatic formation of tactics

    Duncan, H., Bundy, A., Levine, J., Storkey, A. & Pollet, M., 2004, Computer-Supported Mathematical Theory Development’04.

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

  10. Sequential Neural Methods for Likelihood-free Inference

    Durkan, C., Papamakarios, G. & Murray, I., 2018, p. 1-9. 9 p.

    Research output: Contribution to conferencePaper

  11. Cubic-Spline Flows

    Durkan, C., Bekasovs, A., Murray, I. & Papamakarios, G., 15 Jun 2019, First workshop on Invertible Neural Networks and Normalizing Flows: at ICML 2019. 7 p.

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

  12. Neural Spline Flows

    Durkan, C., Bekasovs, A., Murray, I. & Papamakarios, G., 14 Dec 2019, Advances in Neural Information Processing Systems 32 (NeurIPS 2019). Curran Associates Inc, Vol. 32. p. 7509-7520 12 p.

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

  13. Combining two methods of recognizing hand-printed digits

    E. Hinton, G., Williams, C. K. I. & Revow, M. D., 1992, In : Artificial Neural Networks. 2, p. 53-60 8 p.

    Research output: Contribution to journalArticle

  14. A Framework for the Quantitative Evaluation of Disentangled Representations

    Eastwood, C. & Williams, C. K. I., 3 May 2018, Sixth International Conference on Learning Representations (ICLR 2018). 15 p.

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

  15. Data Cleaning using Probabilistic Models of Integrity Constraints

    Eduardo, S. & Sutton, C., 10 Dec 2016, NIPS 2016 Workshop on Artificial Intelligence for Data Science (AI4DataSci 2016). 3 p.

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

  16. Censoring Representations with an Adversary

    Edwards, H. & Storkey, A., 4 Feb 2016, (Accepted/In press) International Conference in Learning Representations (ICLR2016). 14 p.

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

  17. Towards a Neural Statistician

    Edwards, H. & Storkey, A., 26 Apr 2017, 5th International Conference on Learning Representations (ICLR 2017). 13 p.

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

  18. A determinant-free method to simulate the parameters of large Gaussian fields

    Ellam, L., Strathmann, H., Girolami, M. & Murray, I., Aug 2017, In : Stat. 6, 1, p. 271-281 11 p.

    Research output: Contribution to journalArticle

  19. 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

  20. The Shape Boltzmann Machine: A Strong Model of Object Shape

    Eslami, S. M. A., Heess, N., Williams, C. K. I. & Winn, J., Apr 2014, In : International Journal of Computer Vision. 107, 2, p. 155-176 22 p.

    Research output: Contribution to journalArticle

  21. A Generative Model for Parts-based Object Segmentation

    Eslami, S. M. A. & Williams, C. K. I., 2012, Advances in Neural Information Processing Systems 25. Bartlett, P., Pereira, F. C. N., Burges, C. J. C., Bottou, L. & Weinberger, K. Q. (eds.). p. 100-107 8 p.

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

  22. 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

  23. Measuring Symmetry, Asymmetry and Randomness in Neural Network Connectivity

    Esposito, U., Giugliano, M., van Rossum, M. & Vasilaki, E., 9 Jul 2014, In : PLoS ONE. 9, 7, 16 p., 100805.

    Research output: Contribution to journalArticle

  24. The Pascal Visual Object Classes Challenge: A Retrospective

    Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J. & Zisserman, A., Jan 2015, In : International Journal of Computer Vision. 111, 1, p. 98-136 39 p.

    Research output: Contribution to journalArticle

  25. The PASCAL Visual Object Classes (VOC) Challenge

    Everingham, M., van Gool, L., Williams, C. K. I., Winn, J. & Zisserman, A., Jun 2010, In : International Journal of Computer Vision. 88, 2, p. 303-338 36 p.

    Research output: Contribution to journalArticle

  26. The 2005 PASCAL visual object classes challenge

    Everingham, M., Zisserman, A., Williams, C. K. I., Van Gool, L., Allan, M., Bishop, C. M., Chapelle, O., Dalal, N., Deselaers, T., Dorko, G., Duffner, S., Eichhorn, J., Farquhar, J. D. R., Fritz, M., Garcia, C., Griffiths, T., Jurie, F., Keysers, D., Koskela, M., Laaksonen, J. & 14 others, Larlus, D., Leibe, B., Meng, H., Ney, H., Schiele, B., Schmid, C., Seemann, E., Shawe-Taylor, J., Storkey, A., Szedmak, S., Triggs, B., Ulusoy, I., Viitaniemi, V. & Zhang, J., 2006, Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment . Quinonero-Candela, J., Dagan, Magnini, B. & D'Alche-Buc, F. (eds.). Berlin: Springer-Verlag Berlin Heidelberg, p. 117-176 60 p. (Lecture Notes in Computer Science; vol. 3944).

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

  27. The PASCAL visual object classes challenge 2006 (VOC2006) results

    Everingham, M., Zisserman, A., Williams, C. K. I. & Van Gool, L., 2006, 57 p.

    Research output: Working paper

  28. Optimal Well Placement: Paper B003

    Farmer, C., Fowkes, J. & Gould, N., 2010, Proceedings of the 12th European Conference on the Mathematics of Oil Recovery, Oxford.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  29. Fluctuations in the open time of synaptic channels: An application to noise analysis based on charge

    Feldwisch-Drentrup, H., Barrett, A., Smith, M. T. & Rossum, M. V., 2012, In : Journal of Neuroscience Methods. 210, 1, p. 15-21 7 p.

    Research output: Contribution to journalArticle

  30. Combining belief networks and neural networks for scene segmentation

    Feng, X., Williams, C. K. I. & Felderhof, S. N., 1 Apr 2002, In : IEEE Transactions on Pattern Analysis and Machine Intelligence. 24, 4, p. 467-483 17 p.

    Research output: Contribution to journalArticle

  31. Tree-structured belief networks as models of images

    Feng, X. & Williams, C. K. I., 1 Jan 1999, Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470). IET, p. 31-36 5 p.

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

  32. Arc requires PSD95 for assembly into postsynaptic complexes involved with brain disease and intelligence

    Fernandez, E., Collins, M. O., Frank, R. A. W., Zhu, F., Kopanitsa, M. V., Nithianantharajah, J., Lempriere, S., Fricker, D. G., Elsegood, K. A., McLaughlin, C., Croning, M. D. R., McLean, C., Armstrong, J. D., Hill, W. D., Deary, I., Cencelli, G., Bagni, C., Fromer, M., Purcell, S. M., Pocklington, A. J. & 3 others, Choudhary, J. S., Komiyama, N. & Grant, S., 17 Oct 2017, In : Cell Reports. 21, 3, p. 679-691

    Research output: Contribution to journalArticle

  33. Targeted tandem affinity purification of PSD-95 recovers core postsynaptic complexes and schizophrenia susceptibility proteins

    Fernandez, E., Collins, M. O., Uren, R. T., Kopanitsa, M. V., Komiyama, N. H., Croning, M. D. R., Zografos, L., Armstrong, J. D., Choudhary, J. S. & Grant, S. G. N., May 2009, In : Molecular Systems Biology. 5, p. - 17 p., 269.

    Research output: Contribution to journalArticle

  34. Information theoretic novelty detection

    Filippone, M. & Sanguinetti, G., 2010, In : Pattern Recognition. 43, 3, p. 805 - 814 10 p.

    Research output: Contribution to journalArticle

  35. A Perturbative Approach to Novelty Detection in Autoregressive Models

    Filippone, M. & Sanguinetti, G., 1 Mar 2011, In : IEEE Transactions on Signal Processing. 59, 3, p. 1027-1036 10 p.

    Research output: Contribution to journalArticle

  36. Approximate inference of the bandwidth in multivariate kernel density estimation

    Filippone, M. & Sanguinetti, G., 2011, In : Computational statistics & data analysis. 55, 12, p. 3104 - 3122 19 p.

    Research output: Contribution to journalArticle

  37. Structured Prediction of Unobserved Voxels from a Single Depth Image

    Firman, M., Mac Aodha, O., Julier, S. & Brostow, G. J., 12 Dec 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Institute of Electrical and Electronics Engineers (IEEE), p. 5431-5440 10 p.

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

  38. Dictionary of Computer Vision and Image Processing, 2nd Edition

    Fisher, B., Breckon, T. P., Dawson-Howe, K., Fitzgibbon, A., Roberston, C., Trucco, E. & Williams, C. K. I., Dec 2013, 2 ed. Wiley. 384 p.

    Research output: Book/ReportBook

  39. A Bayesian Network Model for Interesting Itemsets

    Fowkes, J. & Sutton, C., 4 Sep 2016, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD 2016). Riva del Garda, Italy: Springer, Cham, p. 410-425 16 p. (Lecture Notes in Computer Science ; vol. 9852).

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

  40. A Subsequence Interleaving Model for Sequential Pattern Mining

    Fowkes, J. & Sutton, C., 13 Aug 2016, KDD '16 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco , United States: ACM, p. 835-844 10 p.

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

  41. TASSAL: Autofolding for Source Code Summarization

    Fowkes, J., Chanthirasegaran, P., Ranca, R., Allamanis, M., Lapata, M. & Sutton, C., May 2016, ICSE '16 Proceedings of the 38th International Conference on Software Engineering Companion. ACM, p. 649-652 4 p.

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

  42. Parameter-Free Probabilistic API Mining across GitHub

    Fowkes, J. & Sutton, C., 1 Nov 2016, FSE 2016: ACM SIGSOFT International Symposium on the Foundations of Software Engineering. Seattle, United States: ACM, p. 254-265 12 p.

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

  43. Autofolding for Source Code Summarization

    Fowkes, J., Chanthirasegaran, P., Ranca, R., Allamanis, M., Lapata, M. & Sutton, C., 6 Feb 2017, In : IEEE Transactions on Software Engineering. 1 p.

    Research output: Contribution to journalArticle

  44. A branch and bound algorithm for the global optimization of Hessian Lipschitz continuous functions

    Fowkes, J., Gould, N. & Farmer, C., 2012, In : Journal of Global Optimization. 56, 4, p. 1791-1815

    Research output: Contribution to journalArticle

  45. Clustered Coding Variants in the Glutamate Receptor Complexes of Individuals with Schizophrenia and Bipolar Disorder

    Frank, R. A. W., McRae, A. F., Pocklington, A. J., Van De Lagemaat, L., Navarro, P., Croning, M. D. R., Komiyama, N. H., Bradley, S. J., Challiss, R. A. J., Armstrong, J. D., Finn, R. D., Malloy, M. P., MacLean, A. W., Harris, S. E., Starr, J., Bhaskar, S. S., Howard, E. K., Hunt, S. E., Coffey, A. J., Ranganath, V. & 7 others, Deloukas, P., Rogers, J., Muir, W. J., Deary, I. J., Blackwood, D. H., Visscher, P. M. & Grant, S. G. N., 29 Apr 2011, In : PLoS ONE. 6, 4, p. - 9 p., e19011.

    Research output: Contribution to journalArticle

  46. Milepost GCC: Machine Learning Enabled Self-tuning Compiler

    Fursin, G., Kashnikov, Y., Memon, A. W., Chamski, Z., Temam, O., Namolaru, M., Yom-Tov, E., Mendelson, B., Zaks, A., Courtois, E., Bodin, F., Barnard, P., Ashton, E., Bonilla, E., Thomson, J., Williams, C. K. I. & O'Boyle, M., Jun 2011, In : International journal of parallel programming. 39, 3, p. 296-327 32 p.

    Research output: Contribution to journalArticle

  47. MILEPOST GCC: machine learning based research compiler

    Fursin, G., Miranda, C., Temam, O., Namolaru, M., Yom-Tov, E., Zaks, A., Mendelson, B., Bonilla, E., Thomson, J., Leather, H., Williams, C. K. I., O'Boyle, M., Barnard, P., Ashton, E., Courtois, E. & Bodin, F., 2008, Proceedings of the GCC Developers' Summit. 13 p.

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

  48. Development of the ASHRAE Global Thermal Comfort Database II

    Földváry, V., Cheung, T., Zhang, H., de Dear, R., Parkinson, T., Arens, E., Chun, C., Schiavon, S., Luo, M., Brager, G., Li, P., Kaam, S., Adebamowo, M. A., Andamon, M. M., Babich, F., Bouden, C., Bukovianska, H., Candido, C., Cao, B., Carlucci, S. & 44 others, Cheong, D. K. W., Choi, J-H., Cook, M., Cropper, P., Deuble, M., Heidari, S., Indraganti, M., Jin, Q., Kim, H., Kim, J., Konis, K., Singh, M. K., Kwok, A., Lamberts, R., Loveday, D., Langevin, J., Manu, S., Moosmann, C., Nicol, F., Ooka, R., Oseland, N. A., Pagliano, L., Petráš, D., Rawal, R., Romero, R., Sekhar, C., Schweiker, M., Tartarini, F., Tanabe, S., Tham, K. W., Teli, D., Toftum, J., Toledo, L., Tsuzuki, K., De Vecchi, R., Wagner, A., Wang, Z., Wallbaum, H., Webb, L., Yang, L., Zhu, Y., Zhai, Y., Zhang, Y. & Zhou, X., Sep 2018, In : Building and Environment. 142, p. 502-512 11 p.

    Research output: Contribution to journalArticle

  49. Consolidation and translation regulation

    Gal-Ben-Ari, S., Kenney, J. W., Ounalla-Saad, H., Taha, E., David, O., Levitan, D., Gildish, I., Panja, D., Pai, B., Wibrand, K., Simpson, T. I., Proud, C. G., Bramham, C. R., Armstrong, J. D. & Rosenblum, K., Sep 2012, In : Learning and Memory. 19, 9, p. 410-22 13 p.

    Research output: Contribution to journalArticle

  50. Bio::Homology::InterologWalk - A Perl module to build putative protein-protein interaction networks through interolog mapping

    Gallone, G., Simpson, T. I., Armstrong, J. D. & Jarman, A. P., Jul 2011, In : BMC Bioinformatics. 12, July, 15 p., 289.

    Research output: Contribution to journalArticle

  51. How do we start? An Approach to Learning Analytics Adoption in Higher Education

    Gasevic, D., Tsai, Y-S., Dawson, S. & Pardo, A., 12 Jun 2019, In : International Journal of Information and Learning Technology. 12 p.

    Research output: Contribution to journalArticle

  52. Scanning techniques for three-dimensional forward-viewing intravascular ultrasound imaging

    Gatzoulis, L., Anderson, T., Pye, S. D., O'Donnell, R., McLean, C. C. & McDicken, W. N., Nov 2000, In : Ultrasound in Medicine & Biology. 26, 9, p. 1461-1474 14 p.

    Research output: Contribution to journalArticle

  53. 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

  54. Complexity and specificity of experimentally induced expectations in motion perception

    Gekas, N., Chalk, M., Seitz, A. R. & Seriès, P., 1 Jan 2013, p. 355. 1 p.

    Research output: Contribution to conferencePoster

  55. Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring

    Georgatzis, K. & Williams, C. K. I., 2015, 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015). 10 p. 77

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

  56. Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring

    Georgatzis, K., Williams, C. K. I. & Hawthorne, C., 20 Aug 2016, Proceedings of Machine Learning for Healthcare 2016: JMLR W&C Track Volume 56. Journal of Machine Learning Research: Workshop and Conference Proceedings, Vol. 56. 16 p.

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

  57. A Software Interface Between the Narrative Language and Bio-PEPA

    Georgoulas, A. & Guerriero, M. L., 2013, In : Electronic Proceedings in Theoretical Computer Science. 293, p. 51-65 15 p.

    Research output: Contribution to journalArticle

  58. ABC-Fun: A Probabilistic Programming Language for Biology

    Georgoulas, A., Hillston, J. & Sanguinetti, G., 2013, Computational Methods in Systems Biology: 11th International Conference, CMSB 2013, Klosterneuburg, Austria, September 22-24, 2013. Proceedings. Gupta, A. & Henzinger, T. A. (eds.). Springer-Verlag GmbH, p. 150-163 14 p. (Lecture Notes in Computer Science; vol. 8130).

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

  59. Unbiased Bayesian Inference for Population Markov Jump Processes via Random Truncations

    Georgoulas, A., Hillston, J. & Sanguinetti, G., 1 Jul 2017, In : Statistics and Computing. 27, 4, p. 991–1002 12 p.

    Research output: Contribution to journalArticle

  60. A subsystems approach for parameter estimation of ODE models of hybrid systems

    Georgoulas, A., Clark, A., Ocone, A., Gilmore, S. & Sanguinetti, G., 19 Aug 2012, In : Electronic Proceedings in Theoretical Computer Science. 92, p. 30-41 12 p.

    Research output: Contribution to journalArticle

  61. Probabilistic Programming Process Algebra

    Georgoulas, A., Hillston, J., Milios, D. & Sanguinetti, G., 8 Sep 2014, Quantitative Evaluation of Systems: 11th International Conference, QEST 2014, Florence, Italy, September 8-10, 2014. Proceedings. Springer International Publishing, p. 249-264 16 p. (Lecture Notes in Computer Science; vol. 8657).

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

  62. ProPPA: Probabilistic Programming for Stochastic Dynamical Systems

    Georgoulas, A., Hillston, J. & Sanguinetti, G., 31 Jan 2018, In : ACM Transactions on Modeling and Computer Simulation. 28, 1, p. 3:1-3:23 23 p., 3.

    Research output: Contribution to journalArticle

  63. Multiple-source cross-validation

    Geras, K. & Sutton, C., 2013, Proceedings of The 30th International Conference on Machine Learning. Journal of Machine Learning Research: Workshop and Conference Proceedings, Vol. 28. p. 1292-1300 9 p.

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

  64. Blending LSTMs into CNNs

    Geras, K. J., Mohamed, A., Caruana, R., Urban, G., Wang, S., Aslan, Ö., Philipose, M., Richardson, M. & Sutton, C., 4 Feb 2016, (Accepted/In press) International Conference on Learning Representations (ICLR Workshop). 13 p.

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

  65. Scheduled Denoising Autoencoders

    Geras, K. & Sutton, C., 10 May 2015, International Conference on Learning Representations (ICLR) 2015. 11 p.

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

  66. Composite denoising autoencoders

    Geras, K. & Sutton, C., 4 Sep 2016, Machine Learning and Knowledge Discovery in Databases: European Conference on Machine Learning and Knowledge Discovery in Databases ECML PKDD 2016. Springer International Publishing, p. 681-696 16 p. (Lecture Notes in Computer Science (LNCS); vol. 9851).

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

  67. MADE: Masked Autoencoder for Distribution Estimation

    Germain, M., Gregor, K., Murray, I. & Larochelle, H., 2015, Proceedings of The 32nd International Conference on Machine Learning. Lille, France: Journal of Machine Learning Research: Workshop and Conference Proceedings, Vol. 37. p. 881-889 9 p.

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

  68. Python in Neuroscience

    Gewaltig, M-O. (ed.), Hines, M. (ed.), Koetter, R. (ed.), Diesmann, M. (ed.), Davison, A. P. (ed.), Muller, E. (ed.) & Bednar, J. (ed.), 2009, Frontiers Media SA.

    Research output: Book/ReportBook

  69. Neuroinformatics and modeling of the basal ganglia: bridging pharmacology and physiology

    Gillies, A. & Willshaw, D., 2007, In : Expert review of medical devices. 4, 5, p. 663-72 10 p.

    Research output: Contribution to journalArticle

  70. Data integration for Classification Problems Employing Gaussian Process Priors

    Girolami, M. & Zhong, M., 2007, Advances in Neural Information Processing Systems 19 (NIPS 2006). MIT Press, 8 p.

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

  71. Unsupervised Monocular Depth Estimation with Left-Right Consistency

    Godard, C., Mac Aodha, O. & Brostow, G. J., 9 Nov 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Institute of Electrical and Electronics Engineers (IEEE), p. 6602-6611 10 p.

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

  72. Digging Into Self-Supervised Monocular Depth Estimation

    Godard, C., Mac Aodha, O., Firman, M. & Brostow, G. J., 22 Jul 2019, (Accepted/In press) The IEEE International Conference on Computer Vision (ICCV). Institute of Electrical and Electronics Engineers (IEEE), 11 p.

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

  73. Towards NeuroML: model description methods for collaborative modelling in neuroscience.

    Goddard, N., Hucka, M., Howell, F., Cornelis, H., Shankar, K. & Beeman, D., 1 Aug 2001, In : Philosophical Transactions of the Royal Society B: Biological Sciences. 356, 1412, p. 1209-1228 20 p.

    Research output: Contribution to journalArticle

  74. 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

  75. eCAT: Online electronic lab notebook for scientific research

    Goddard, N., Macneil, R. & Ritchie, J., 2009, In : Automated Experimentation. 1, p. 1-7 7 p., 4.

    Research output: Contribution to journalArticle

  76. Associating What and Where Using Temporal Cues

    Goddard, N. H., 1993, Proceedings of the 15th Annual Conference of the Cognitive Science Society. Vol. 15.

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

  77. Representing and recognizing event sequences

    Goddard, N. H., 1989, Proc. AAAI Workshop on Neural Architectures for Computer Vision.

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

  78. Machine learning and multimedia content generation for energy demand reduction

    Goddard, N. H., Moore, J. D., Sutton, C. A., Lovell, H. & Webb, J., 2012, Sustainable Internet and ICT for Sustainability (SustainIT), 2012. Institute of Electrical and Electronics Engineers (IEEE), p. 1-5 5 p.

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

  79. NeuroML for plug and play neuronal modeling

    Goddard, N. H., Beeman, D., Cannon, R., Cornelis, H., Gewaltig, M. ., Hood, G., Howell, D. F., Rogister, P., Schutter, E. D., Shankar, K. & Hucka, M., Jun 2002, In : Neurocomputing. 44-46, p. 1077-1081 5 p.

    Research output: Contribution to journalArticle

  80. Functional magnetic resonance imaging dataset analysis

    Goddard, N. H., Hood, G., Cohen, J. D., Nystrom, L. E., Eddy, W. F., Genovese, C. R. & Noll, D. C., 2000, Industrial strength parallel computing. Koniges, A. E. (ed.). Morgan Kaufman, p. 431-452 23 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  81. Regression with input-dependent noise: A Gaussian process treatment

    Goldberg, P. W., Williams, C. K. I. & Bishop, C. M., 1997, Advances in Neural Information Processing Systems 10 (NIPS 1997). MIT Press, p. 493-499 7 p.

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

  82. Pitfalls of Thresholding Statistical Maps in Presurgical fMRI Mapping

    Gorgolewski, K. J., Bastin, M., Rigolo, L., Soleiman, H. A., Pernet, C., Storkey, A. & Golby, A., 2011.

    Research output: Contribution to conferencePoster

  83. Reliability of single subject fMRI in the context of presurgical planning

    Gorgolewski, K. J., Storkey, A., Bastin, M. & Pernet, C., 2012, 18th Annual Meeting of the Organization for Human Brain Mapping.

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

  84. Adaptive thresholding for reliable topological inference in single subject fMRI analysis

    Gorgolewski, K. J., Storkey, A. J., Bastin, M. E. & Pernet, C. R., 25 Aug 2012, In : Frontiers in Human Neuroscience. 6, 245.

    Research output: Contribution to journalArticle

  85. Single subject fMRI test-retest reliability metrics and confounding factors

    Gorgolewski, K. J., Storkey, A. J., Bastin, M. E., Whittle, I. & Pernet, C., 2013, In : NeuroImage. 69, p. 231-43 13 p.

    Research output: Contribution to journalArticle

  86. Probabilistic Programming with Densities in SlicStan: Efficient, Flexible, and Deterministic

    Gorinova, M. I., Gordon, A. D. & Sutton, C., 2 Jan 2019, In : Proceedings of the ACM on Programming Languages (PACMPL). 3, POPL, p. 35:1-35:30 30 p., 35.

    Research output: Contribution to journalArticle

  87. 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

  88. Continuously tempered Hamiltonian Monte Carlo

    Graham, M. & Storkey, A., 9 Dec 2016.

    Research output: Contribution to conferenceAbstract

  89. Continuously tempered Hamiltonian Monte Carlo

    Graham, M. & Storkey, A., 15 Aug 2017, The Conference on Uncertainty in Artificial Intelligence (UAI 2017). Sydney, Australia, 10 p.

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

  90. Asymptotically exact inference in differentiable generative models

    Graham, M. & Storkey, A., 22 Apr 2017, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017. Journal of Machine Learning Research: Workshop and Conference Proceedings, p. 499-508 10 p.

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

  91. Synapse proteomics of multiprotein complexes: en route from genes to nervous system diseases

    Grant, S. G. N., Marshall, M. C., Page, K-L., Cumiskey, M. A. & Armstrong, J. D., 2005, In : Human Molecular Genetics. 14, suppl 2, p. R225-R234 10 p.

    Research output: Contribution to journalArticle

  92. The Organization and integrative function of the post-synaptic proteome

    Grant, S., Husi, H., Choudhary, J., Cumiskey, M., Blackstock, W. & Armstrong, D., 2004, Excitatory-Inhibitory Balance: Synapses, Circuits, Systems. KLUWER ACADEMIC/PLENUM PUBL, p. 13-44 32 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

  93. Ordering cancer mutational profiles of cross-sectional copy number alterations

    Graudenzi, A., Caravagna, G., Bocicor, I., Cava, C., Antoniotti, M. & Mauri, G., Jan 2016, In : International Journal of Data Mining and Bioinformatics. 15, 1, p. 59-83 25 p.

    Research output: Contribution to journalArticle

  94. A multiscale model of intestinal crypts dynamics

    Graudenzi, A., Caravagna, G., De Matteis, G., Mauri, G. & Antoniotti, M., 2012, Proceedings of the Italian Workshop on Artificial Life and Evolutionary Computation (WIVACE 2012). 13 p.

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

  95. Resource-Efficient Feature Gathering at Test Time

    Gray, G. & Storkey, A., 2016.

    Research output: Contribution to conferencePoster

  96. Chapter Six – Genetic Analysis of Drosophila Circadian Behavior in Seminatural Conditions

    Green, E. W., O'Callaghan, E. K., Pegoraro, M., Armstrong, J. D., Costa, R. & Kyriacou, C. P., 2015, Circadian Rhythms and Biological Clocks, Part A. Sehgal, A. (ed.). Academic Press, p. 121-133 13 p. (Methods in Enzymology; vol. 559).

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

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