Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells

    Clark, S. J., Argelaguet, R., Kapourani, A., Stubbs, T. M., Lee, H. J., Alda-Catalinas, C., Krueger, F., Sanguinetti, G., Kelsey, G., Marioni, J. C., Stegle, O. & Reik, W., 22 Feb 2018, In : Nature Communications. 9, 17 p., 781.

    Research output: Contribution to journalArticle

  2. riboviz: analysis and visualization of ribosome profiling datasets

    Carja, O., Xing, T., Wallace, E. W. J., Plotkin, J. B. & Shah, P., 25 Oct 2017, In : BMC Bioinformatics. 18, 461

    Research output: Contribution to journalArticle

  3. qpMerge: Merging different peptide isoforms using a motif centric strategy

    Hindle, M. M., Le Bihan, T., Krahmer, J., Martin, S. F., Noordally, Z. B., Simpson, T. I. & Millar, A. J., 5 Apr 2016, (Submitted) bioRxiv, at Cold Spring Harbor Laboratory, 9 p.

    Research output: Working paper

  4. puma: a Bioconductor package for propagating uncertainty in microarray analysis

    Pearson, R., Liu, X., Sanguinetti, G., Milo, M., Lawrence, N. & Rattray, M., 2009, In : BMC Bioinformatics. 10, 1, 10 p.

    Research output: Contribution to journalArticle

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

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

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

    Research output: Contribution to journalArticle

  16. 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

  17. 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

  18. Volume transmission as a new homeostatic mechanism

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

    Research output: Contribution to conferencePoster

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. 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

  29. 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

  30. 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

  31. 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

  32. 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

  33. 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

  34. 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

  35. 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

  36. 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

  37. 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

  38. 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

  39. 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

  40. 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

  41. 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

  42. 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

  43. 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

  44. 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

  45. 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

  46. 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

  47. 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

  48. 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

  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

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