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Infinite Sparse Factor Analysis and InfiniteIndependent Components…
https://mlg.eng.cam.ac.uk/zoubin/papers/ica07knowles.pdf27 Jan 2023: Technical Report 1, Gatsby Computational Neuroscience Unit (2005). 6. Ghahramani, Z., Griffiths, T., Sollich, P.: Bayesian nonparametric latent featuremodels. -
SMEM Algorithm for Mixture Models
https://mlg.eng.cam.ac.uk/pub/pdf/UedNakGha98a.pdf13 Feb 2023: Hinton zoubin@gatsby.uc1. ac.uk g.hinton@ucl.ac.uk. Gatsby Computational Neuroscience Unit, University College London 17 Queen Square, London WC1N 3AR, UK. -
nipsderiv.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/SolMurLeietal03.pdf13 Feb 2023: LeithHamilton Institute,National Univ. of. Ireland, Maynooth,Co. Kildare, Irelanddoug.leith@may.ie. C. E. RasmussenGatsby Computational Neuroscience Unit,. ... Afurther practical reason is related to the fact that the computational expense of -
MCMC for doubly-intractable distributions Iain MurrayGatsby…
https://mlg.eng.cam.ac.uk/zoubin/papers/doubly_intractable.pdf27 Jan 2023: MCMC for doubly-intractable distributions. Iain MurrayGatsby Computational. Neuroscience UnitUniversity College London. -
Variational Inference for BayesianMixtures of Factor Analysers Zoubin …
https://mlg.eng.cam.ac.uk/zoubin/papers/nips99.pdf27 Jan 2023: Beal. Gatsby Computational Neuroscience UnitUniversity College London. 17 Queen Square, London WC1N 3AR, England{zoubin,m.beal}@gatsby.ucl.ac.uk http://www.gatsby.ucl.ac.uk. -
Split and Merge EM Algorithm for Improving Gaussian Mixture Density…
https://mlg.eng.cam.ac.uk/pub/pdf/UedNakGha00b.pdf13 Feb 2023: ZOUBIN GHAHRAMANI AND GEOFFREY E. HINTONGatsby Computational Neuroscience Unit, University College London, 17 Queen Square, London WC1N 3AR, UK. ... He is currently a Lecturer atthe Gatsby Computational Neuroscience Unit at University CollegeLondon, with -
nlds-ftp.dvi
https://mlg.eng.cam.ac.uk/zoubin/papers/nlds-ftp.pdf27 Jan 2023: RoweisGatsby Computational Neuroscience UnitUniversity College LondonLondon WC1N 3AR, U.K.http://www.gatsby.ucl.ac.uk/AbstractThe Expectation{Maximization (EM) algorithm is an iterative pro-cedure for maximum likelihood parameter estimation -
ibpnips4.dvi
https://mlg.eng.cam.ac.uk/zoubin/papers/ibp-nips05.pdf27 Jan 2023: Infinite Latent Feature Modelsand the Indian Buffet Process. Thomas L. Griffiths Zoubin GhahramaniCognitive and Linguistic Sciences Gatsby Computational Neuroscience UnitBrown University, Providence RI University College London, Londontom -
iMGPE.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/RasGha02.pdf13 Feb 2023: Infinite Mixtures of Gaussian Process Experts. Carl Edward RasmussenandZoubin GhahramaniGatsby Computational Neuroscience Unit. ... 1. Initialize indicator variables i to a single value (or a few values if individual GPsare to be kept small for -
Cambridge Machine Learning Group Publications
https://mlg.eng.cam.ac.uk/pub/authors/13 Feb 2023: Abstract: Existing methods for estimating uncertainty in deep learning tend to require multiple forward passes, making them unsuitable for applications where computational resources are limited. ... Abstract: On a mathematical level, most computational -
Bayesian Sets Zoubin Ghahramani∗ and Katherine A. HellerGatsby…
https://mlg.eng.cam.ac.uk/pub/pdf/GhaHel06.pdf13 Feb 2023: Bayesian Sets. Zoubin Ghahramani and Katherine A. HellerGatsby Computational Neuroscience Unit. -
The Infinite Hidden Markov Model Matthew J. Beal Zoubin ...
https://mlg.eng.cam.ac.uk/zoubin/papers/ihmm.pdf27 Jan 2023: Gatsby Computational Neuroscience UnitUniversity College London. 17 Queen Square, London WC1N 3AR, Englandhttp://www.gatsby.ucl.ac.uk. -
Unsupervised Learning Variational Approximations Zoubin…
https://mlg.eng.cam.ac.uk/zoubin/course05/lect7var.pdf27 Jan 2023: Unsupervised Learning. Variational Approximations. Zoubin Ghahramanizoubin@gatsby.ucl.ac.uk. Gatsby Computational Neuroscience Unit, andMSc in Intelligent Systems, Dept Computer Science. -
iMGPE.dvi
https://mlg.eng.cam.ac.uk/zoubin/papers/iMGPE.pdf27 Jan 2023: Infinite Mixtures of Gaussian Process Experts. Carl Edward RasmussenandZoubin GhahramaniGatsby Computational Neuroscience Unit. ... 1. Initialize indicator variables i to a single value (or a few values if individual GPsare to be kept small for -
zgl.dvi
https://mlg.eng.cam.ac.uk/zoubin/papers/zgl.pdf27 Jan 2023: Gatsby Computational Neuroscience Unit, University College London, London WC1N 3AR, UK. ... For in-stance, obtaining a single labeled example for protein shapeclassification, which is one of the grand challenges of bio-logical and computational science, -
A Probabilistic Model for Online DocumentClustering with Application…
https://mlg.eng.cam.ac.uk/zoubin/papers/zgy-nips04.pdf27 Jan 2023: Zoubin Ghahramani. Gatsby Computational Neuroscience Unit. University College LondonLondon WC1N 3AR, UK. -
Draft version; accepted for NIPS*03 Warped Gaussian Processes Edward…
https://mlg.eng.cam.ac.uk/zoubin/papers/gpwarp.pdf27 Jan 2023: Gatsby Computational Neuroscience UnitUniversity College London. 17 Queen Square, London WC1N 3AR, UK{snelson,zoubin }@gatsby.ucl.ac.uk. ... Since the computational limiter to a GP is inverting thecovariance matrix, adding a few extra parameters into the -
Cambridge Machine Learning Group Publications
https://mlg.eng.cam.ac.uk/pub/13 Feb 2023: Many existing algorithms restart training for each new hyperparameter choice, at considerable computational cost. ... We compare this scheme against natural baselines in literature along with stochastic variational GPs (SVGPs) along with an extensive -
A Probabilistic Model for Online Document Clustering with Application …
https://mlg.eng.cam.ac.uk/pub/pdf/ZhaGhaYan04a.pdf13 Feb 2023: Jian Zhang††School of Computer ScienceCargenie Mellon University. Pittsburgh, PA 15213jian.zhang@cs.cmu.edu. Zoubin Ghahramani†‡‡ Gatsby Computational Neuroscience Unit. -
Warped Gaussian Processes Edward Snelson∗ Carl Edward Rasmussen†…
https://mlg.eng.cam.ac.uk/pub/pdf/SneRasGha04.pdf13 Feb 2023: Warped Gaussian Processes. Edward Snelson Carl Edward Rasmussen† Zoubin Ghahramani. Gatsby Computational Neuroscience UnitUniversity College London. ... Since the computational limiter to a GP is inverting thecovariance matrix, adding a few extra
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