Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. The proteomes of neurotransmitter receptor complexes form modular networks with distributed functionality underlying plasticity and behaviour

    Pocklington, A. J., Cumiskey, M., Armstrong, J. D. & Grant, S. G. N., 17 Jan 2006, In : Molecular Systems Biology. 2, 14 p., 2006.0023.

    Research output: Contribution to journalArticle

  2. The shape variational autoencoder: A deep generative model of part-segmented 3D objects

    Nash, C. & Williams, C. K. I., Aug 2017, Symposium on Geometry Processing (SGP 2017). 11 p.

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

  3. 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

  4. The winged helix transcription factor Foxg1 facilitates retinal ganglion cell axon crossing of the ventral midline in the mouse

    Pratt, T., Tian, N. M. M-L., Simpson, T. I., Mason, J. O. & Price, D. J., 2004, In : Development. 131, 15, p. 3773-84 12 p.

    Research output: Contribution to journalArticle

  5. Theoretical models of synaptic short term plasticity

    Hennig, M. H., 2013, In : Frontiers in Computational Neuroscience. 7, 10 p., 45.

    Research output: Contribution to journalArticle

  6. Thermal comfort requirements: A study of people with multiple sclerosis

    Webb, L., Parsons, K. C. & Hodder, S. G., 1999, In : ASHRAE Transactions. 105, p. 648-660 13 p.

    Research output: Contribution to journalArticle

  7. Third cumulant of the total transmission of diffuse waves

    van Rossum, M. C. W., de Boer, J. F. & Nieuwenhuizen, T. M., 1 Aug 1995, In : Physical Review E - Statistical, Nonlinear and Soft Matter Physics. 52, 2, p. 2053-2065 13 p.

    Research output: Contribution to journalArticle

  8. Tilt Aftereffects In A Self-Organizing Model Of The Primary Visual Cortex

    Bednar, J. A. & Miikkulainen, R., 2000, In : Neural Computation. 12, p. 1721-1740 20 p.

    Research output: Contribution to journalArticle

  9. To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions

    Lopez Guevara, T., Pucci, R., Taylor, N., Gutmann, M., Ramamoorthy, S. & Subr, K., 2018. 5 p.

    Research output: Contribution to conferencePaper

  10. Topographic Analysis of Correlated Components

    Sasaki, H., Gutmann, M. U., Shouno, H. & Hyvärinen, A., 2012, Proc. Asian Conference on Machine Learning (ACML). Journal of Machine Learning Research - Proceedings Track, p. 365-378 14 p. 25

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

  11. Topographica

    Bednar, J. A., 4 Mar 2014, Encyclopedia of Computational Neuroscience. Jaeger, D. & Jung, R. (eds.). New York, NY: Springer New York, p. 1-5 5 p.

    Research output: Chapter in Book/Report/Conference proceedingEntry for encyclopedia/dictionary

  12. Topographica: Computational Modeling of Neural Maps

    Ball, C. E. & Bednar, J. A., 2009.

    Research output: Contribution to conferencePoster

  13. Topographica: building and analyzing map-level simulations from Python, C/C++, MATLAB, NEST, or NEURON components

    Bednar, J. A., 2009, In : Frontiers in Neuroinformatics. 3, 8, p. 1-9 9 p., 8.

    Research output: Contribution to journalArticle

  14. Toward data representation with spiking neurons

    Gutmann, M. & Aihara, K., 2008, In : Artificial Life and Robotics. 12, 1, p. 223-226 4 p.

    Research output: Contribution to journalArticle

  15. Towards Multimodal Deep Learning for Activity Recognition on Mobile Devices

    Radu, V., Lane, N. D., Bhattacharya, S., Mascolo, C., Marina, M. K. & Kawsar, F., 12 Sep 2016, Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. New York, NY, USA: ACM, p. 185-188 4 p. (UbiComp '16).

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

  16. 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 : Philos Trans R Soc Lond B Biol Sci. 356, 1412, p. 1209-1228 20 p.

    Research output: Contribution to journalArticle

  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. Towards a quantitative model of the post-synaptic proteome

    Sorokina, O., Sorokin, A. & Armstrong, J. D., 2011, In : Molecular BioSystems. 7, 10, p. 2813-2823 11 p.

    Research output: Contribution to journalArticle

  19. Towards a virtual fly brain

    Armstrong, J. D. & van Hemert, J. I., Jun 2009, In : Philosophical Transactions A: Mathematical, Physical and Engineering Sciences. 367, 1896, p. 2387-2397 11 p.

    Research output: Contribution to journalArticle

  20. Towards tracking homeostais on high-density multi-electrode arrays

    Panas, D., Maccione, A., Berdondini, L. & Hennig, M., Sep 2012.

    Research output: Contribution to conferencePoster

  21. Tracking of Individual Mice in a Social Setting Using Video Tracking Combined with RFID tags

    Armstrong, J. D., Acevedo-Arozena, A., Bains, R. S., Cater, H., Chartsias, A., Nolan, P., Sneddon, D., Sillito, R. & Wells, S., 25 May 2016, Proceedings of Measuring Behavior 2016: 10th International Conference on Methods and Techniques in Behavioral Research. Spink, A. J., Riedel, G., Zhou, L., Teekens, L., Albatal, R. & Gurrin, C. (eds.). Dublin, Ireland, p. 413-416 4 p.

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

  22. Tract shape modelling provides evidence of topological change in corpus callosum genu during normal ageing

    Bastin, M. E., Piatkowski, J. P., Storkey, A. J., Brown, L. J., MacLullich, A. M. J. & Clayden, J. D., 15 Oct 2008, In : NeuroImage. 43, 1, p. 20-28 9 p.

    Research output: Contribution to journalArticle

  23. 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

  24. TractoR: Magnetic Resonance Imaging and Tractography with R

    Clayden, J. D., Maniega, S. M., Storkey, A. J., King, M. D., Bastin, M. E. & Clark, C. A., Oct 2011, In : Journal of statistical software. 44, 8, p. 1-18 18 p.

    Research output: Contribution to journalArticle

  25. Training Deep Convolutional Neural Networks to Play Go

    Clark, C. & Storkey, A., 2015, Proceedings of the 32nd International Conference on Machine Learning (IMCL 2015). Lille, France: PMLR, Vol. 37. p. 1766-1774 9 p.

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

  26. Transcript Profiling and Inference of Escherichia coli K-12 ArcA Activity across the Range of Physiologically Relevant Oxygen Concentrations

    Rolfe, M. D., Ter Beek, A., Graham, A. I., Trotter, E. W., Asif, H. M. S., Sanguinetti, G., de Mattos, J. T., Poole, R. K. & Green, J., 25 Mar 2011, In : Journal of Biological Chemistry. 286, 12, p. 10147-10154 8 p.

    Research output: Contribution to journalArticle

  27. Transcription factor binding predicts histone modifications in human cell lines

    Benveniste, D., Sonntag, H-J., Sanguinetti, G. & Sproul, D., 16 Sep 2014, In : Proceedings of the National Academy of Sciences. 111, 37, p. 13367-72 6 p.

    Research output: Contribution to journalArticle

  28. Transcription rate strongly affects splicing fidelity and cotranscriptionality in budding yeast

    Aslanzadeh, V., Huang, Y., Sanguinetti, G. & Beggs, J., 2018, In : Genome Research. 12 p.

    Research output: Contribution to journalArticle

  29. Transcriptional Analysis of Gli3 Mutants Identifies Wnt Target Genes in the Developing Hippocampus

    Hasenpusch-Theil, K., Magnani, D., Amaniti, E-M., Han, L., Armstrong, D. & Theil, T., Dec 2012, In : Cerebral Cortex. 22, 12, p. 2878-2893 16 p.

    Research output: Contribution to journalArticle

  30. Transformation Equivariant Boltzmann Machines

    Kivinen, J. J. & Williams, C. K. I., 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., Duch, W., Girolami, M. & Kaski, S. (eds.). Springer-Verlag GmbH, p. 1-9 9 p. (Lecture Notes in Computer Science; vol. 6791).

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

  31. Transition of Escherichia coli from Aerobic to Micro-aerobic Conditions Involves Fast and Slow Reacting Regulatory Components

    Partridge, J. D., Sanguinetti, G., Dibden, D. P., Roberts, R. E., Poole, R. K. & Green, J., Apr 2007, In : Journal of Biological Chemistry. 282, 15, p. 11230-11237 8 p.

    Research output: Contribution to journalArticle

  32. Transmission of population-coded information

    Renart, A. & van Rossum, M. C. W., Feb 2012, In : Neural Computation. 24, 2, p. 391-407 17 p.

    Research output: Contribution to journalArticle

  33. Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3

    Huynh-Thu, V. A. & Sanguinetti, G., 2019, Gene Regulatory Networks: Methods and Protocols. Sanguinetti, G. & Huynh-Thu, V. A. (eds.). New York, NY: Springer New York LLC, p. 217-233 17 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  34. 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

  35. Trends and challenges in Computational RNA biology

    Selega, A. & Sanguinetti, G., 7 Dec 2016, In : Genome Biology. 17, 253, p. 1-4 4 p.

    Research output: Contribution to journalMeeting abstract

  36. Truncated covariance matrices and Toeplitz methods in Gaussian processes

    Storkey, AJ., 1999, Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470) (Volume:1 ) . EDISON: INST ELECTRICAL ENGINEERS INSPEC INC, p. 55-60 6 p. (IEE CONFERENCE PUBLICATIONS).

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

  37. Tuning Curve Sharpening for orientation selectivity: coding efficiency and the impact of correlations

    Series, P., Latham, P. & Pouget, A., 2004, In : Nature Neuroscience. 7, p. 1129-35

    Research output: Contribution to journalArticle

  38. U-check: Model Checking and Parameter Synthesis under Uncertainty

    Bortolussi, L., Milios, D. & Sanguinetti, G., 2015, Quantitative Evaluation of Systems: 12th International Conference, QEST 2015, Madrid, Spain, September 1-3, 2015, Proceedings. Springer International Publishing, p. 89-104 16 p. (Lecture Notes in Computer Science; vol. 9259).

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

  39. 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

  40. Understanding Neural Maps with Topographica

    Bednar, J. A., 2008, In : Brains, Minds & Media. 1

    Research output: Contribution to journalArticle

  41. Understanding Neural Population Coding: Information Theoretic Insights from the Auditory System

    Onken, A., Karunasekara, P. P. C. R., Kayser, C. & Panzeri, S., 19 Oct 2014, In : Advances in Neuroscience. 2014, p. 1-14 15 p., 907851.

    Research output: Contribution to journalArticle

  42. Understanding synaptic pathways - modelling Parkinson's Disease

    Heil, K. F., Sorokina, O., Hellgren-kotaleski, J. & Armstrong, JD., 18 Jun 2014.

    Research output: Contribution to conferencePoster

  43. Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners

    Chen, Y., Singla, A., Mac Aodha, O., Perona, P. & Yue, Y., 8 Dec 2018, Advances in Neural Information Processing Systems 31. Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N. & Garnett, R. (eds.). Neural Information Processing Systems, Vol. 31. p. 1476-1486 11 p.

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

  44. Unified pre- and postsynaptic long-term plasticity enables reliable and flexible learning

    Costa, R. P., Froemke, R. C., Sjöström, P. J. & Van Rossum, M. C. W., 26 Aug 2015, In : eLIFE. 4, 24 p.

    Research output: Contribution to journalArticle

  45. Universal fluctuations in a simple disordered system

    Nieuwenhuizen, T. M. & Van Rossum, M., 1991, In : Physics letters a. 160, 5, p. 461 - 464 4 p.

    Research output: Contribution to journalArticle

  46. 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

  47. Unsupervised deduplication using cross-field dependencies

    Hall, R., Sutton, C. & McCallum, A., 2008, Proceedings of the 14th ACM SIGKDD international conference on Knowledge Discovery and Data mining (KDD '08). New York, NY, USA: ACM, p. 310-317 8 p.

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

  48. Unsupervised spike sorting for large scale, high density multielectrode arrays

    Hilgen, G., Sindaci, M. S., Pirmoradian, S., Muthmann, J-O., Kepiro, I., Ullo, S., Juarez Ramirez, C., Encinas, A. P., Maccione, A., Berdondini, L., Murino, V., Sona, D., Cella Zanacchi, F., Sernagor, E. & Hennig, M. H., 7 Mar 2017, In : Cell Reports. 18, 10, p. 2521-2532 12 p.

    Research output: Contribution to journalArticle

  49. Using BRIE to Detect and Analyze Splicing Isoforms in scRNA-Seq Data

    Huang, Y. & Sanguinetti, G., 2019, Computational Methods for Single-Cell Data Analysis. Yuan, G-C. (ed.). New York, NY: Springer New York LLC, p. 175-185 11 p. (Methods in Molecular Biology; vol. 1935).

    Research output: Chapter in Book/Report/Conference proceedingChapter

  50. Using Bayesian neural networks to classify segmented images

    Vivarelli, F. & Williams, C. K. I., 1 Jul 1997, Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440). IET, p. 268-273 6 p.

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

  51. Using Generative Models for Handwritten Digit Recognition

    Revow, M., Williams, C. K. I. & Hinton, G. E., Jun 1996, In : IEEE Transactions on Pattern Analysis and Machine Intelligence. 18, 6, p. 592-606 15 p.

    Research output: Contribution to journalArticle

  52. 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

  53. Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains

    Onken, A., Liu, J. K., Karunasekara, P. P. C. R., Delis, I., Gollisch, T. & Panzeri, S., 4 Nov 2016, In : PLoS Computational Biology. 12, 11, p. 1-46 46 p., e1005189.

    Research output: Contribution to journalArticle

  54. Using a neural net to instantiate a deformable model

    Williams, C. K. I., Revow, M. & Hinton, G. E., 1995, Advances in neural information processing systems. p. 965-972 8 p.

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

  55. Using affective and behavioural sensors to explore aspects of collaborative music making

    Morgan, E., Gunes, H. & Bryan-Kinns, N., Oct 2015, In : International Journal of Human-Computer Studies. 82, p. 31-47 17 p.

    Research output: Contribution to journalArticle

  56. Using the Nyström Method to Speed Up Kernel Machines

    Williams, C. K. I. & Seeger, M., 2001, Advances in Neural Information Processing Systems 13 (NIPS 2000). Leen, T. K., Dietterich, T. G. & Tresp, V. (eds.). MIT Press, p. 682-688 7 p.

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

  57. 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

  58. VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning

    Srivastava, A., Valkov, L., Russell, C., Gutmann, M. & Sutton, C., 9 Dec 2017, Advances in Neural Information Processing Systems 30 (NIPS 2017). Long Beach, CA, USA: Curran Associates Inc, p. 3308-3318 18 p.

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

  59. VIBES: A Variational Inference Engine for Bayesian Networks

    Bishop, C. M., Spiegelhalter, D. J. & Winn, J., 2002, Advances in Neural Information Processing Systems 15 (NIPS 2002). 8 p.

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

  60. Validating a standardised test battery for synesthesia: Does the Synesthesia Battery reliably detect synesthesia?

    Carmichael, D. A., Down, M. P., Shillcock, R. C., Eagleman, D. M. & Simner, J., May 2015, In : Consciousness and Cognition. 33, p. 375-385 11 p.

    Research output: Contribution to journalArticle

  61. Validity conditions for moment closure approximations in stochastic chemical kinetics

    Schnoerr, D., Sanguinetti, G. & Grima, R., 1 Jan 2014, In : Journal of Chemical Physics. 141, 8, 084103.

    Research output: Contribution to journalArticle

  62. Variational Bayesian Model Selection for Mixture Distributions

    Corduneanu, A. & Bishop, C. M., 2001, Proceedings Eighth International Conference on Artificial Intelligence and Statistics. Morgan Kaufmann, p. 27-34 8 p.

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

  63. Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations

    Zammit-Mangion, A., Sanguinetti, G. & Kadirkamanathan, V., 1 Jul 2012, In : IEEE Transactions on Signal Processing. 60, 7, p. 3449-3459 11 p.

    Research output: Contribution to journalArticle

  64. Variational Learning in Graphical Models and Neural Networks

    Bishop, C., 1998, ICANN 98: Proceedings of the 8th International Conference on Artificial Neural Networks, Skövde, Sweden, 2–4 September 1998. Niklasson, L., Boden, M. & Ziemke, T. (eds.). Springer London, p. 13-22 10 p. (Perspectives in Neural Computing).

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

  65. Variational Message Passing

    Winn, J. & Bishop, C., 2005, In : Journal of Machine Learning Research. 6, p. 661-694 34 p.

    Research output: Contribution to journalArticle

  66. Variational Noise-Contrastive Estimation

    Rhodes, B. & 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. 2741-2750 14 p.

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

  67. Variational Principal Components

    Bishop, C. M., 1 Jan 1999, Proceedings Ninth International Conference on Artificial Neural Networks, ICANN'99. IEE, p. 509-514 6 p.

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

  68. Variational Relevance Vector Machines

    Bishop, C. M. & Tipping, M. E., 1 Jan 2000, Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, p. 46-53 8 p.

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

  69. Variational inference for Markov jump processes

    Opper, M. & Sanguinetti, G., 2008, Advances in Neural Information Processing Systems 20 (NIPS 2007). Platt, J. C., Koller, D., Singer, Y. & Roweis, S. T. (eds.). p. 1105-1112 8 p.

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

  70. Verifying Anti-Security Policies Learnt from Android Malware Families

    Chen, W., Sutton, C., Aspinall, D., Gordon, A., Shen, Q. & Muttik, I., 21 Oct 2015, Fourth International Seminar on Program Verification, Automated Debugging and Symbolic Computation. 6 p.

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

  71. Very Predictive Ngrams for Space-Limited Probabilistic Models

    Cohen, P. R. & Sutton, C. A., 2003, Advances in Intelligent Data Analysis V: 5th International Symposium on Intelligent Data Analysis, IDA 2003, Berlin, Germany, August 28-30, 2003. Proceedings. R. Berthold, M., Lenz, H-J., Bradley, E., Kruse, R. & Borgelt, C. (eds.). Springer-Verlag GmbH, p. 134-142 9 p. (Lecture Notes in Computer Science; vol. 2810).

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

  72. Virtual Fly Brain

    Osumi-Sutherland, D., Milyaev, N., Reeve, S., Armstrong, J. D. & Ashburner, M., Dec 2010, In : Journal of neurogenetics. 24, p. 49-50 2 p.

    Research output: Contribution to journalMeeting abstract

  73. Virtual Fly Brain 3D Interaction Tool

    Milyaev, N., Armstrong, D., Osumi-Sutherland, D., Ashburner, M., Baldock, R. A., Burton, N. & Husz, Z. L., Dec 2010, In : Journal of neurogenetics. 24, p. 50-50 1 p.

    Research output: Contribution to journalMeeting abstract

  74. Virtual fly brain - Using OWL to support the mapping and genetic dissection of the drosophila brain

    Osumi-Sutherland, D., Costa, M., Court, R. & O'Kane, C. J., 2014, In : CEUR Workshop Proceedings. 1265, p. 85-96 12 p.

    Research output: Contribution to journalArticle

  75. Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single Image

    Romaszko, L., Williams, C. K. I., Moreno, P. & Kohli, P., 23 Jan 2018, ICCV 2017 Workshop on Geometry Meets Deep Learning. Institute of Electrical and Electronics Engineers (IEEE), p. 940-948 9 p.

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

  76. Vision2Sensor: Knowledge Transfer Across Sensing Modalities for Human Activity Recognition

    Radu, V. & Henne, M., 9 Sep 2019, In : PACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 3, 3, p. 84:1-84:21 21 p., 84.

    Research output: Contribution to journalArticle

  77. Visual Aftereffects, Models of

    Bednar, J., 2014, Encyclopedia of Computational Neuroscience. Jaeger, D. & Jung, R. (eds.). Springer New York, p. 1-8

    Research output: Chapter in Book/Report/Conference proceedingEntry for encyclopedia/dictionary

  78. Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection

    Kivinen, J., Williams, C. K. I. & Heess, N., 2014, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics. Reykjavik, Iceland: Journal of Machine Learning Research: Workshop and Conference Proceedings, Vol. 33. p. 512-521 10 p.

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

  79. Visual Cortex as a General-Purpose Information-Processing Device

    Bednar, J. A., 2012, Computer Vision – ECCV 2012. Workshops and Demonstrations: Florence, Italy, October 7-13, 2012, Proceedings, Part I. Fusiello, A., Murino, V. & Cucchiara, R. (eds.). Springer Berlin Heidelberg, p. 480-485 6 p.

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

  80. Volume transmission as a new homeostatic mechanism

    Sweeney, Y. A., Hellgren-Kotaleski, J. & Hennig, M., 2013.

    Research output: Contribution to conferencePoster

  81. Weak Epistasis May Drive Adaptation in Recombining Bacteria

    Arnold, B. J., Gutmann, M., Grad, Y. H., Sheppard, S. K., Corander, J., Lipsitch, M. & Hanage, W. P., 1 Mar 2018, In : Genetics. 208, 3, p. 1247-1260 39 p.

    Research output: Contribution to journalArticle

  82. What can MaxEnt reveal about high-density recordings and what can high-density recordings reveal about MaxEnt?

    Panas, D., Maccione, A., Berdondini, L. & Hennig, M., 2011, In : BMC Neuroscience. 12, Supplement 1, 2 p., P146.

    Research output: Contribution to journalMeeting abstract

  83. What, if anything, are topological maps for?

    Wilson, S. P. & Bednar, J. A., 11 Feb 2015, In : Developmental neurobiology.

    Research output: Contribution to journalArticle

  84. When Training and Test Sets Are Different: Characterizing Learning Transfer

    Storkey, A., Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A. & Lawrence, ND., Dec 2008, Dataset Shift in Machine Learning. Cambridge: Yale University Press in association with the Museum of London, p. 3-28 26 p. (Neural Information Processing Series).

    Research output: Chapter in Book/Report/Conference proceedingChapter

  85. 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

  86. Wide-band information transmission at the calyx of Held

    Hennig, M., Graham, B. P., Yang, Z., Postlethwaite, M. & Forsythe, I. D., Apr 2009, In : Neural Computation. 21, 4, p. 991-1017 27 p.

    Research output: Contribution to journalLetter

  87. Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio

    Jastrzębski, S., Kenton, Z., Arpit, D., Ballas, N., Fischer, A., Bengio, Y. & Storkey, A., Oct 2018, Proceedings of 27th International Conference on Artificial Neural Networks. Rhodes, Greece: Springer, Cham, p. 392-402 10 p.

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

  88. Word Storms: Multiples of Word Clouds for Visual Comparison of Documents

    Castella, Q. & Sutton, C. A., 2014, Proceedings of the 23rd international conference on World wide web. International World Wide Web Conferences Steering Committee, p. 665-676 12 p.

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

  89. Wrattler: Reproducible, live and polyglot notebooks

    Petricek, T., Geddes, J. & Sutton, C., 2018, 10th USENIX Workshop on Theory and Practice of Provenance (TaPP 2018). London, UK: Usenix, p. 1-4 4 p.

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

  90. 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

  91. fMRI correlates of state and trait effects in subjects at genetically enhanced risk of schizophrenia

    Whalley, H. C., Simonotto, E., Flett, S., Marshall, I., Ebmeier, K. P., Owens, D. G. C., Goddard, N. H., Johnstone, E. C. & Lawrie, S. M., Mar 2004, In : Brain. 127, Pt 3, p. 478-90 13 p.

    Research output: Contribution to journalArticle

  92. iBehave - applications of supervised machine learning to behaviour analysis.

    Heward, J. A., Crook, P. A., Lukins, T. C. & Armstrong, D., 2008, Proceedings of Measuring Behaviour 2008. 6th International Conference on Methods and Techniques in Behavioural Research. Spink, A., Ballintijn, M., Bogers, N., Grieco, F., Loijens, L., Noldus, L., Smit, G. & Zimmerman, P. (eds.). p. 314-315 2 p.

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

  93. mRNA Cap Methyltransferase, RNMT-RAM, Promotes RNA Pol II-Dependent Transcription

    Varshney, D., Lombardi, O., Schweikert, G., Dunn, S., Suska, O. & Cowling, V. H., 2 May 2018, In : Cell Reports. 23, 5, p. 1530-1542 14 p.

    Research output: Contribution to journalArticle