Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Localisation microscopy using quantum dots

    Mandula, O., Williams, C. K. I. & Heintzmann, R., 2010.

    Research output: Contribution to conferencePoster

  2. Localisation microscopy with quantum dots using non-negative matrix factorisation

    Mandula, O., Sestak, I. S., Heintzmann, R. & Williams, C. K. I., 6 Oct 2014, In : Optics Express. 22, 20, p. 24594-24605 12 p.

    Research output: Contribution to journalArticle

  3. Locating objects with diffuse light

    den Outer, P. N., Van Rossum, M., Nieuwenhuizen, T. M. & Lagendijk, A., 1994, OSA Proceedings on Advances in Optical Imaging and Photon Migration. SPIE, p. 297-302 4 p.

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

  4. Long-range downstream enhancers are essential for Pax6 expression

    Kleinjan, D. A., Seawright, A., Mella, S., Carr, C. B., Tyas, D. A., Simpson, I., Mason, J. O., Price, D. J. & van Heyningen, V., 2006, In : Developmental Biology. 299, 2, p. 563-81 19 p.

    Research output: Contribution to journalArticle

  5. Low-voltage activated Kv1.1 subunits are crucial for the processing of sound source location in the lateral superior olive in mice

    Karcz, A., Hennig, M., Robbins, C. A., Tempel, B. L., Rübsamen, R. & Kopp-Scheinpflug, C., Mar 2011, In : Journal of Physiology. 589, 5, p. 1143-1157

    Research output: Contribution to journalArticle

  6. M3D: a kernel-based test for spatially correlated changes in methylation profiles

    Mayo, T., Schweikert, G. & Sanguinetti, G., 6 Mar 2015, In : Bioinformatics. 31, 6, p. 809-816 8 p.

    Research output: Contribution to journalArticle

  7. 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

  8. MCMC for doubly-intractable distributions

    Murray, I., Ghahramani, Z. & MacKay, D. J. C., 2006, Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligence (UAI-06). AUAI Press, p. 359-366 8 p.

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

  9. MEK Inhibitors Reverse cAMP-Mediated Anxiety in Zebrafish

    Lundegaard, P. R., Anastasaki, C., Grant, N. J., Sillito, R. R., Zich, J., Zeng, Z., Paranthaman, K., Larsen, A. P., Armstrong, J. D., Porteous, D. J. & Patton, E. E., 22 Oct 2015, In : Chemistry and Biology.

    Research output: Contribution to journalArticle

  10. 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

  11. 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

  12. MIMO INTERACTIONS IN SAMPLED DATA SYSTEMS

    Brocal, M. S. & Oyarzún, D. R., 2005, In : IFAC Proceedings Volumes (IFAC-PapersOnline). 38, 1, p. 119 - 124 6 p.

    Research output: Contribution to journalArticle

  13. MMDiff: quantitative testing for shape changes in ChIP-Seq data sets

    Schweikert, G., Cseke, B., Clouaire, T., Bird, A. & Sanguinetti, G., 2013, In : BMC Genomics. 14, 1, 17 p., 826.

    Research output: Contribution to journalArticle

  14. MMG: a probabilistic tool to identify submodules of metabolic pathways

    Sanguinetti, G., Noirel, J. & Wright, P. C., Apr 2008, In : Bioinformatics. 24, 8, p. 1078-1084 7 p.

    Research output: Contribution to journalArticle

  15. Machine Learning Markets

    Storkey, A. J., 22 Jun 2011, Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. p. 716-724 9 p. (JMLR Workshop and Conference Proceedings; vol. 15).

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

  16. Machine Learning Methods in Statistical Model Checking and System Design – Tutorial

    Bortolussi, L., Milios, D. & Sanguinetti, G., 2015, Runtime Verification: 6th International Conference, RV 2015, Vienna, Austria, September 22-25, 2015. Proceedings. Bartocci, E. & Majumdar, R. (eds.). Cham: Springer International Publishing, p. 323-341 19 p. (Lecture Notes in Computer Science; vol. 9333).

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

  17. 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

  18. Magnification factors for the GTM algorithm

    Bishop, C. M., Svensén, M. & Williams, C. K. I., 1 Jan 1997, Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440). Institute of Electrical and Electronics Engineers (IEEE), p. 64–69 6 p.

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

  19. Magnification factors for the SOM and GTM algorithms

    Bishop, C. M., Svensén, M. & Williams, C. K. I., 1997, Proceedings 1997 Workshop on Self-Organizing Maps. p. 333–338 6 p.

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

  20. Major Depression Impairs the Use of Reward Values for Decision-Making

    Rupprechter, S., Stankevicius, A., Huys, Q. J. M., Steele, J. D. & Series, P., 14 Sep 2018, In : Scientific Reports. 9 p., 13798 (2018) .

    Research output: Contribution to journalArticle

  21. Maladaptive decision-making in a rat version of the Iowa Gambling Task

    Valton, V., Marchand, A., Dellu-Hagedom, F. & Series, P., Feb 2012. 1 p.

    Research output: Contribution to conferencePoster

  22. Markov Chain Truncation for Doubly-Intractable Inference

    Wei, C. & Murray, I., 15 Apr 2017, In : Journal of Machine Learning Research: Workshop and Conference Proceedings. 54, p. 776-784 9 p.

    Research output: Contribution to journalArticle

  23. Masked Autoregressive Flow for Density Estimation

    Papamakarios, G., Pavlakou, T. & Murray, I., 9 Dec 2017, Advances in Neural Information Processing Systems 30. Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S. & Garnett, R. (eds.). Long Beach, United States: Curran Associates Inc, p. 2335-2344 10 p.

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

  24. Matching Models Across Abstraction Levels with Gaussian Processes

    Caravagna, G., Bortolussi, L. & Sanguinetti, G., 4 Sep 2016, Computational Methods in Systems Biology: 14th International Conference, CMSB 2016, Cambridge, UK, September 21-23, 2016, Proceedings. Bartocci, E., Lio, P. & Paoletti, N. (eds.). Cham: Springer International Publishing, p. 49-66 18 p. (Lecture Notes in Computer Science; vol. 9859).

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

  25. MeCP2 recognizes cytosine methylated tri-nucleotide and di-nucleotide sequences to tune transcription in the mammalian brain

    Lagger, S., Connelly, J. C., Schweikert, G., Webb, S., Selfridge, J., Ramsahoye, B. H., Yu, M., He, C., Sanguinetti, G., Sowers, L. C., Walkinshaw, M. D., Bird, A. & Greally, J. M. (ed.), 12 May 2017, In : PLoS Genetics. 13, 5, p. 26 1 p., e1006793.

    Research output: Contribution to journalArticle

  26. Measuring Affect for the Study and Enhancement of Co-present Creative Collaboration

    Morgan, E., Gunes, H. & Bryan-Kinns, N., 1 Sep 2013, Affective Computing and Intelligent Interaction (ACII), 2013 Humaine Association Conference on. Institute of Electrical and Electronics Engineers (IEEE), p. 659-664 6 p.

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

  27. 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

  28. Mechanisms for Stable, Robust, and Adaptive Development of Orientation Maps in the Primary Visual Cortex

    Stevens, J-L., Law, J., Antolik, J. & Bednar, J. A., 2 Oct 2013, In : Journal of Neuroscience. 33, 40, p. 15747-15766 20 p.

    Research output: Contribution to journalArticle

  29. Mechanistic links between cellular trade-offs, gene expression, and growth

    Weisse, A., Oyarzun, D., Danos, V. & Swain, P., 3 Mar 2015, In : Proceedings of the National Academy of Sciences. 112, 9, p. E1038-1047 9 p.

    Research output: Contribution to journalArticle

  30. Melissa: Bayesian clustering and imputation of single cell methylomes

    Kapourani, C. A. & Sanguinetti, G., 21 Mar 2019, In : Genome Biology. 20, 1, 15 p., 61.

    Research output: Contribution to journalArticle

  31. Memory Retention and Spike-Timing-Dependent Plasticity

    Billings, G. & van Rossum, M. C. W., Jun 2009, In : Journal of Neurophysiology. 101, 6, p. 2775-2788 14 p.

    Research output: Contribution to journalArticle

  32. Merged consensus clustering to assess and improve class discovery with microarray data

    Simpson, T. I., Armstrong, J. D. & Jarman, A. P., 3 Dec 2010, In : BMC Bioinformatics. 11, December, 12 p., 590.

    Research output: Contribution to journalArticle

  33. Metamorphosis of the Mushroom Bodies; Large-Scale Rearrangements of the Neural Substrates for Associative Learning and Memory in Drosophila

    Armstrong, J. D., de Belle, J. S., Wang, Z. & Kaiser, K., 1998, In : Learning and Memory. 5, 1, p. 102-114 13 p.

    Research output: Contribution to journalArticle

  34. MicroRNA-34a Acutely Regulates Synaptic Efficacy in the Adult Dentate Gyrus In Vivo

    Berentsen, B., Patil, S., Rønnestad, K., Goff, K. M., Pajak, M., Simpson, T. I., Wibrand, K. & Bramham, C. R., 21 Nov 2019, In : Molecular Neurobiology. 14 p.

    Research output: Contribution to journalArticle

  35. Microrobotically Fabricated Biological Scaffolds for Tissue Engineering

    Nain, A. S., Chung, F., Rule, M., Jadlowiec, J. A., Campbell, P. G., Amon, C. & Sitti, M., 1 Apr 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers (IEEE), p. 1918-1923 6 p.

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

  36. 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

  37. 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

  38. 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

  39. 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

  40. Mirror Neurons, Prediction and Hemispheric Coordination; The Prioritizing of Intersubjectivity over 'Intrasubjectivity'

    Shillcock, R., Thomas, J. & Bailes, R., 24 Nov 2018, In : Axiomathes. p. 1-15 15 p.

    Research output: Contribution to journalArticle

  41. Mirror symmetry on K3 surfaces as a hyper-Kaehler rotation

    Bruzzo, U. & Sanguinetti, G., 1998, In : Letters in mathematical physics. 45, 4, p. 295-301 7 p.

    Research output: Contribution to journalArticle

  42. Misleading Learners: Co-opting Your Spam Filter

    Nelson, B., Barreno, M., Jack Chi, F., Joseph, A. D., Rubinstein, B. I. P., Saini, U., Sutton, C., Tygar, J. D. & Xia, K., 2009, Machine Learning in Cyber Trust: Security, Privacy, and Reliability. Springer US, p. 17-51 35 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  43. Missing Data in Kernel PCA

    Sanguinetti, G. & Lawrence, N. D., 2006, Machine Learning: ECML 2006: 17th European Conference on Machine Learning Berlin, Germany, September 18-22, 2006 Proceedings. Fürnkranz, J., Scheffer, T. & Spiliopoulou, M. (eds.). Springer Berlin Heidelberg, p. 751-758 8 p. (Lecture Notes in Computer Science; vol. 4212).

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

  44. Mixed vine copulas as joint models of spike counts and local field potentials

    Onken, A. & Panzeri, S., 11 Dec 2016, Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain. Barcelona, Spain: Neural Information Processing Systems, p. 1325-1333 9 p.

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

  45. Mixed-species RNA-seq for elucidating non-cell-autonomous control of gene transcription

    Qiu, J., Dando, O., Baxter, P., Hasel, P., Heron, S., Simpson, T. & Hardingham, G., 24 Sep 2018, In : Nature Protocols. 26 p.

    Research output: Contribution to journalArticle

  46. Mixture Model on Graphs: A Probabilistic Model for Network-Based Analysis of Proteomic Data

    Noirel, J., Sanguinetti, G. & Wright, P., 2010, Systems Biology for Signaling Networks: Part II. Choi, S. (ed.). Springer New York, p. 371-397 27 p. (Systems Biology).

    Research output: Chapter in Book/Report/Conference proceedingChapter

  47. Mixture Regression for Covariate Shift

    Sugiyama, M. & Storkey, A. J., 2007, Advances in Neural Information Processing Systems 19. Schölkopf, B., Platt, J. C. & Hoffman, T. (eds.). MIT Press, p. 1337-1344 8 p.

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

  48. Mode Normalization

    Deecke, L., Murray, I. & Bilen, H., 9 May 2019, Proceedings of the Seventh International Conference on Learning Representations. New Orleans, Louisiana, USA, 12 p.

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

  49. Model Criticism in Latent Space

    Seth, S., Murray, I. & Williams, C. K. I., 11 Jun 2019, In : Bayesian analysis. 14, 3, p. 703-725 23 p.

    Research output: Contribution to journalArticle

  50. Model Reduction of Genetic-Metabolic Networks via Time Scale Separation

    Kuntz, J., Oyarzún, D. & Stan, G-B., 2014, A Systems Theoretic Approach to Systems and Synthetic Biology I: Models and System Characterizations. Kulkarni, V. V., Stan, G-B. & Raman, K. (eds.). Dordrecht: Springer Netherlands, p. 181-210 30 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  51. Model Selection and Adaptation of Hyperparameters

    Rasmussen, C. E. & Williams, C. K. I., Nov 2005, Gaussian Processes for Machine Learning. Rasmussen, C. E. & Williams, C. K. I. (eds.). MIT Press, p. 105-128 24 p. (Adaptive Computation and Machine Learning series).

    Research output: Chapter in Book/Report/Conference proceedingChapter

  52. Model selection in systems and synthetic biology

    Kirk, P., Thorne, T. & Stumpf, M. P. H., 1 Aug 2013, In : Current opinion in biotechnology. 24, 4, p. 767-774 8 p.

    Research output: Contribution to journalArticle

  53. Model-based machine learning

    Bishop, C. M., 2013, In : Philosophical Transactions A: Mathematical, Physical and Engineering Sciences. 371, 1984, 20120222.

    Research output: Contribution to journalArticle

  54. Modeling Conflict Dynamics with Spatio-temporal Data

    Zammit-Mangion, A., Dewar, M., Kadirkamanathan, V., Flesken, A. & Sanguinetti, G., 2013, Springer International Publishing. 82 p. (Modeling Conflict Dynamics with Spatio-temporal Data)

    Research output: Book/ReportBook

  55. Modeling Cortical Maps with Topographica

    Bednar, J. A., Choe, Y., Paula, J. D., Miikkulainen, R., Provost, J. & Tversky, T., 2004, In : Neurocomputing. 58-60, p. 1129-1135 7 p.

    Research output: Contribution to journalArticle

  56. Modeling Self-Organization in the Visual Cortex

    Miikkulainen, R., Bednar, J., Choe, Y. & Sirosh, J., 1999, Kohonen Maps. Oja, E. & Kaski, S. (eds.). ELSEVIER SCIENCE PUBL B V, p. 243-252

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

  57. Modeling Trait Anxiety: From Computational Processes to Personality

    Raymond, J. G., Steele, J. D. & Seriès, P., 23 Jan 2017, In : Frontiers in psychiatry. 8, p. 1-19 19 p., 1.

    Research output: Contribution to journalArticle

  58. Modeling large cortical networks with growing self-organizing maps

    Bednar, J. A., Kelkar, A. & Miikkulainen, R., 1 Jun 2002, In : Neurocomputing. p. 315-321 6 p.

    Research output: Contribution to journalArticle

  59. Modeling maladaptive decision-making in a rat version of the Iowa Gambling Task

    Valton, V., Marchand, A., Dellu-Hagedom, F. & Series, P., 18 Jul 2011.

    Research output: Contribution to conferencePoster

  60. Modeling maladaptive decision-making in a rat version of the Iowa Gambling Task

    Valton, V., Marchand, A., Dellu-hagedorn, F. & Seriès, P., 1 Jan 2011, In : BMC Neuroscience. 12, Suppl 1, P294.

    Research output: Contribution to journalMeeting abstract

  61. Modeling maladaptive decision-making in a rat version of the Iowa Gambling Task

    Valton, V., Marchand, A., Dellu-Hagedom, F. & Series, P., 2011.

    Research output: Contribution to conferencePoster

  62. Modeling self-organization of color-opponent receptive fields and laterally connected orientation maps and color blobs in V1

    De Paula, J. B., Bednar, J. A. & Miikkulainen, R., 2004.

    Research output: Contribution to conferenceAbstract

  63. Modeling self-organizing tri-chromatic color selective regions in primary visual cortex

    De Paula, J., Bednar, J. & Miikkulainen, R., 1 Jan 2007, In : BMC Neuroscience. 8, Suppl 2, p. S24

    Research output: Contribution to journalArticle

  64. Modeling spike-count dependence structures with multivariate Poisson distributions

    Onken, A. & Obermayer, K., 11 Jul 2008, p. 1-2. 2 p.

    Research output: Contribution to conferenceAbstract

  65. Modeling the Emergence of Whisker Direction Maps in Rat Barrel Cortex

    Wilson, S. P., Law, J., Mitchinson, B., Prescott, T. J. & Bednar, J. A., Jan 2010, In : PLoS ONE. 5, 1, 11 p., e8778.

    Research output: Contribution to journalArticle

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

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

    Research output: Contribution to conferenceAbstract

  67. Modeling the visual cortex using the Topographica cortical map simulator

    Bednar, J., Choe, Y., De Paula, J., Miikkulainen, R. & Provost, J., 2005.

    Research output: Contribution to conferencePoster

  68. Modelling Conditional Probability Distributions for Periodic Variables

    Bishop, C. & Nabney, I., 1997, Mathematics of Neural Networks: Models, Algorithms and Applications. Ellacott, S., Mason, J. & Anderson, I. (eds.). Springer US, p. 118-122 5 p. (Operations Research/Computer Science Interfaces Series; vol. 8).

    Research output: Chapter in Book/Report/Conference proceedingChapter

  69. Modelling acoustic feature dependencies with artificial neural networks: Trajectory-RNADE

    Uria, B., Murray, I., Renals, S., Valentini-Botinhao, C. & Bridle, J., 2015, Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on . IEEE Signal Processing Society Press, p. 4465-4469 5 p.

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

  70. Modelling biological systems with delays in Bio-PEPA

    Caravagna, G. & Hillston, J., 2010, Proceedings of MeCBIC 2010. p. 85-101 17 p. (Electronic Proceedings in Theoretical Computer Science; vol. 40).

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

  71. Modelling face adaptation aftereffects

    Zhao, C. R., Hancock, P. & Bednar, J. A., 2008.

    Research output: Contribution to conferencePoster

  72. Modelling homeostatic control of intrinsic excitability in single neurons

    Sweeney, Y. A., Hennig, M. & Hellgren-Kotaleski, J., 2012.

    Research output: Contribution to conferencePoster

  73. Modelling homeostatic control of intrinsic excitability in single neurons

    Sweeney, Y. A., Hellgren-Kotaleski, J. & Hennig, M., 2012.

    Research output: Contribution to conferencePoster

  74. Modelling mechanotransduction in primary sensory endings

    Suslak, T., McKay-Fletcher, J. A., Armstrong, D., Jarman, A. & Bewick, G., 2013.

    Research output: Contribution to conferencePoster

  75. Modelling motion primitives and their timing in biologically executed movements

    Williams, B., Toussaint, M. & Storkey, A. J., 2008, Advances in Neural Information Processing Systems 20. Platt, J. C., Koller, D., Singer, Y. & Roweis, S. T. (eds.). Curran Associates Inc, p. 1609-1616 8 p.

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

  76. Modelling socio-economic and energy data to generate business-as-usual scenarios for carbon emissions

    Roberts, S. H., Axon, C. J., Goddard, N. H., Foran, B. D. & Warr, B. S., 10 Jan 2019, In : Journal of Cleaner Production. 207, p. 980 - 997 18 p.

    Research output: Contribution to journalArticle

  77. Modelling surround modulation in the LGN

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

    Research output: Contribution to conferencePoster

  78. Modelling the acquisition of lexical segmentation

    Cairns, P., Shillcock, R., Chater, N. & Levy, J., 1994, The Proceedings of the 26th Annual Child Language Research Forum. Clark, E. V. (ed.). Vol. 26. p. 32-41 10 p.

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

  79. Modelling transcriptional regulation using Gaussian processes

    Lawrence, N. D., Sanguinetti, G. & Rattray, M., 2006, Advances in Neural Information Processing Systems. p. 785-792 8 p.

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

  80. Modelling-based experiment retrieval: A case study with gene expression clustering

    Blomstedt, P., Dutta, R., Seth, S., Brazma, A. & Kaski, S., 1 May 2016, In : Bioinformatics. 32, 9, p. 1388-1394 7 p.

    Research output: Contribution to journalArticle

  81. Models of reading: Principles for model development

    Shillcock, R., Roberts, M., Kreiner, H. & Obregón, M., 1 Aug 2009. 1 p.

    Research output: Contribution to conferenceAbstract

  82. Modularity and the processing of closed-class words

    Shillcock, R. C. & Bard, E. G., Dec 1993, Cognitive models of speech processing: The Second Sperlonga Meeting. Altmann, G. & Shillcock, R. (eds.). Lawrence Erlbaum Associates, p. 163-185 22 p.

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

  83. Monitoring brain activity and behaviour in freely moving Drosophila larvae using bioluminescence

    Marescotti, M., Lagogiannis, K., Webb, B., Davies, R. W. & Armstrong, J. D., 18 Jun 2018, In : Scientific Reports. 8, 1, 10 p., 9246.

    Research output: Contribution to journalArticle

  84. Moonshine: Distilling with Cheap Convolutions

    Crowley, E., Gray, G. & Storkey, A., 2018, Thirty-second Conference on Neural Information Processing Systems (NIPS 2018). Montreal, Canada, 11 p.

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

  85. More Semantics More Robust: Improving Android Malware Classifiers

    Chen, W., Aspinall, D., Gordon, A., Sutton, C. & Muttik, I., 18 Jul 2016, 9th ACM Conference on Security and Privacy in Wireless and Mobile Networks. Darmstadt, Germany: ACM, p. 147-158 12 p.

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

  86. Motion aftereffects in a self-organizing model of the primary visual cortex

    Ball, C. & Bednar, J., 2006, p. 34.

    Research output: Contribution to conferencePoster

  87. Multi-Task Time Series Analysis applied to Drug Response Modelling

    Bird, A., Williams, C. K. I. & Hawthorne, C., 18 Apr 2019, Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics. Lawrence, N. & Reid, M. (eds.). PMLR, Vol. 89. 10 p. (Proceedings of Machine Learning Research).

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

  88. Multi-period Trading Prediction Markets with Connections to Machine Learning

    Hu, J. & Storkey, A., 2014, Proceedings of ICML 2014. Journal of Machine Learning Research: Workshop and Conference Proceedings, Vol. 32. p. 1773-1781 9 p.

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

  89. Multi-task Gaussian Process Learning of Robot Inverse Dynamics

    Chai, K. M., Williams, C. K. I., Klanke, S. & Vijayakumar, S., 2008, Proc. Advances in Neural Information Processing Systems (NIPS '08). p. 8 8 p.

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

  90. Multi-task Gaussian Process Prediction

    Bonilla, E. V., Chai, K. M. A. & Williams, C. K. I., 2008, Advances in Neural Information Processing Systems 20. NIPS Foundation, p. 153-160 8 p.

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

  91. Multi-task learning for pKa prediction

    Skolidis, G., Hansen, K., Sanguinetti, G. & Rupp, M., 2012, In : Journal of computer-Aided molecular design. 26, 7, p. 883-895 13 p.

    Research output: Contribution to journalArticle

  92. Multidisciplinary research: should effort be the measure of success?

    Buswell, R. A., Webb, L., Mitchell, V. & Mackley, K. L., 13 Jul 2016, In : Building Research and Information. 45, 5, p. 539-555 17 p.

    Research output: Contribution to journalArticle

  93. Multimodal Deep Learning for Activity and Context Recognition

    Radu, V., Tong, C., Bhattacharya, S., Lane, N., Mascolo, C., Marina, M. K. & Kawsar, F., 8 Jan 2018, In : PACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 1, 4, p. 157:1-157:27 27 p., 157.

    Research output: Contribution to journalArticle

  94. Multiobjective optimization of gene circuits for metabolic engineering

    Otero-Muras, I., Mannan, A. A., Banga, J. R. & Oyarzún, D. A., 26 Dec 2019, In : IFAC-PapersOnLine. 52, 26, p. 13-16 4 p.

    Research output: Contribution to journalArticle

  95. Multiphase flow monitoring in oil pipelines

    Bishop, C. M., 1995, Applications of Neural Networks: Section B. Murray, A. (ed.). Springer US, p. 133-155 23 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter

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