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Bayesian Sets Zoubin Ghahramani∗ and Katherine A. HellerGatsby…
https://mlg.eng.cam.ac.uk/pub/pdf/GhaHel06.pdf13 Feb 2023: Behavioral and Brain. Sciences, 24:629–641.[6] Tong, S. (2005). Personal communication. -
zglactive.dvi
https://mlg.eng.cam.ac.uk/zoubin/papers/zglactive.pdf27 Jan 2023: Combining Active Learning and Semi-Supervised LearningUsing Gaussian Fields and Harmonic Functions. Xiaojin Zhu. ZHUXJ@CS.CMU.EDUJohn Lafferty. LAFFERTY@CS.CMU.EDU. Zoubin Ghahramani. ZOUBIN@GATSBY.UCL.AC.UKSchool of Computer Science, Carnegie -
Continuous Relaxations for Discrete Hamiltonian Monte Carlo
https://mlg.eng.cam.ac.uk/pub/pdf/ZhaSutSto12a.pdf13 Feb 2023: 1 Introduction. Discrete undirected graphical models have seen wide use in natural language processing [11, 24] andcomputer vision [19]. ... Weinberger, editors,Advances in Neural Information Processing Systems 24, pages 2744–2752. 2011. -
Bayesian Hierarchical Clustering Katherine A. Heller…
https://mlg.eng.cam.ac.uk/zoubin/papers/bhcnew.pdf27 Jan 2023: 1 2 3 4 5 6 8 9 10 7 11 12 13 14 15 16 18 20 19 17 21 22 23 24 25 26 270. ... 0.2231.24. 3.6. 59.9. 0 1 2 3 4 5 6 74. -
The Infinite Hidden Markov Model Matthew J. Beal Zoubin ...
https://mlg.eng.cam.ac.uk/zoubin/papers/ihmm.pdf27 Jan 2023: The Infinite Hidden Markov Model. Matthew J. Beal Zoubin Ghahramani Carl Edward Rasmussen. Gatsby Computational Neuroscience UnitUniversity College London. 17 Queen Square, London WC1N 3AR, Englandhttp://www.gatsby.ucl.ac.uk. {m.beal,zoubin,edward -
Bayesian Hierarchical Clustering Katherine A. Heller…
https://mlg.eng.cam.ac.uk/zoubin/papers/icml05heller.pdf27 Jan 2023: 1 2 3 4 5 6 8 9 10 7 11 12 13 14 15 16 18 20 19 17 21 22 23 24 25 26 270. ... 0.2231.24. 3.6. 59.9. 0 1 2 3 4 5 6 74. -
Sequential Decisions
https://mlg.eng.cam.ac.uk/zoubin/SALD/week13sequential.pdf27 Jan 2023: solutions – the latter relating to “improper” priors! 24. Appendix: Background on the Von Neumann - Morgenstern theory of cardinal. -
Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning
https://mlg.eng.cam.ac.uk/pub/pdf/ZhuKanGha04a.pdf13 Feb 2023: 50.27 (86) 0.24 (92) 0.15 0.18 0.40 (85) 0.02 0.12 0.09. ... 0.27 (26) 0.13 (25) 0.03 0.11 0.31 (24) -0.89 -0.80 -0.65100 64.6 2.1 59.0 3.6 58.5 2.9 -
A Brief Overview of Nonparametric Bayesian Models NIPS 2009 ...
https://mlg.eng.cam.ac.uk/zoubin/talks/nips09npb.pdf27 Jan 2023: 21). Given s, the distribution of Z becomes:. p( Z | x , s, µ ( 1 : ) ) p( Z | x , µ ( 1 : ) ) 1µ I (0 s µ ) (24). -
Bayesian Classifier Combination Hyun-Chul Kim Zoubin GhahramaniKorea…
https://mlg.eng.cam.ac.uk/pub/pdf/KimGha12.pdf13 Feb 2023: Bagging predictors. Ma-chine Learning 24, 123–140. Breiman, L., October 2001. Random forests. ... Vol. 24. pp. 791–798. Takács, G., Pilászy, I., Németh, B., Tikk, D., 2007.
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