Search

27 Jan 2023: Unsupervised Learning. Graphical Models. Zoubin Ghahramanizoubin@gatsby.ucl.ac.uk. Gatsby Computational Neuroscience Unit, andMSc in Intelligent Systems, Dept Computer Science.

13 Feb 2023: Gatsby Computational Neuroscience UnitUniversity College London. 17 Queen Square, London WC1N 3AR, Englandhttp://www.gatsby.ucl.ac.uk.

27 Jan 2023: Bayesian Sets. Zoubin Ghahramani and Katherine A. HellerGatsby Computational Neuroscience Unit.

27 Jan 2023: Gatsby Computational Neuroscience Unit, University College London, London WC1N 3AR, UK. ... As a final word on computational efficiency, wenote that after adding query and its answer to , inthe next iteration we will need to compute 8z 0. -

27 Jan 2023: School of Computer Science ‡ Gatsby Computational Neuroscience UnitCargenie Mellon University University College London.

27 Jan 2023: Gatsby Computational Neuroscience Unit1. University College London, UK. Center for Automated Learning and DiscoveryCarnegie Mellon University, USA. ... Gatsby Computational Neuroscience Unit Technical Report GCNU-TR 2005-001. Other. • Müller, P.

27 Jan 2023: Rong Jin Zoubin Ghahramani† School of Computer Science †Gatsby Computational Neuroscience Unit Carnegie Mellon University University College London Pittsburgh, PA 15213, USA London WC1N 3AR, UK rong@cs.cmu.edu

13 Feb 2023: A Nonparametric Bayesian Approach toModeling Overlapping Clusters. Katherine A. HellerGatsby Computational Neuroscience Unit. ... R. Neal. Markov chain sampling methods for Dirichletprocess mixture models. Journal of Computational andGraphical Statistics,

27 Jan 2023: Compact approximations to Bayesian predictive distributions. Edward Snelson snelson@gatsby.ucl.ac.ukGatsby Computational Neuroscience Unit, University College London, Queen Square, London WC1N 3AR, UK. ... Zoubin Ghahramani zoubin@gatsby.ucl.ac.ukGatsby

13 Feb 2023: 1 Department of Computer Science,POSTECH, Republic of Korea. {emtidi, sybang, seungjin}@postech.ac.kr2 Gatsby Computational Neuroscience Unit,University College London, 17 Queen Square,.

27 Jan 2023: Hidden Common Cause Relations inRelational Learning. Ricardo Silva. Gatsby Computational Neuroscience UnitUCL, London, UK WC1N 3ARrbas@gatsby.ucl.ac.uk. ... Wei ChuCenter for Computational Learning SystemsColumbia University, New York, NY 10115.

13 Feb 2023: Nonparametric Transforms of Graph Kernelsfor Semi-Supervised Learning. Xiaojin Zhu † Jaz Kandola‡ Zoubin Ghahramani‡† John Lafferty †. †School of Computer Science ‡Gatsby Computational Neuroscience UnitCarnegie Mellon University

27 Jan 2023: Rosalind W. Picard picard@media.mit.eduMIT Media Laboratory, Cambridge, MA, 02139 USA. Zoubin Ghahramani zoubin@gatsby.ucl.ac.ukGatsby Computational Neuroscience Unit, UCL, 17 Queen Square, London, WC1N 3AR, UK. ... 2.3. Computational Issues. To compute

27 Jan 2023: MATTHEW J. BEAL and ZOUBIN GHAHRAMANIGatsby Computational Neuroscience Unit, UCL, UK. ... A reasonable comparison should take into account the tradeoff betweenaccuracy and computational complexity.

27 Jan 2023: Online Variational Bayesian Learning. Zoubin GhahramaniGatsby Computational Neuroscience Unit. University College London.

13 Feb 2023: Rosalind W. Picard picard@media.mit.eduMIT Media Laboratory, Cambridge, MA, 02139 USA. Zoubin Ghahramani zoubin@gatsby.ucl.ac.ukGatsby Computational Neuroscience Unit, UCL, 17 Queen Square, London, WC1N 3AR, UK. ... 2.3. Computational Issues. To compute

13 Feb 2023: 2005). Infinite latent fea-ture models and the Indian buffet process. TR 2005-001,Gatsby Computational Neuroscience Unit.

13 Feb 2023: c Gatsby Computational Neuroscience Unit, UCL, London WC1N 3AR, UK. Received 11 August 2004; received in revised form 15 September 2005Available online 18 November 2005.

13 Feb 2023: Bayesian Learning in Undirected Graphical Models:Approximate MCMC algorithms. Iain Murray and Zoubin GhahramaniGatsby Computational Neuroscience Unit.

27 Jan 2023: Unsupervised Learning. A Belief Propagation Demo. Zoubin Ghahramanizoubin@gatsby.ucl.ac.uk. Gatsby Computational Neuroscience Unit, andMSc in Intelligent Systems, Dept Computer Science.