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  1. Results that match 1 of 2 words

  2. BIOINFORMATICS Vol. 20 no. 9 2004, pages 1361–1372DOI:…

    https://mlg.eng.cam.ac.uk/pub/pdf/RanAngGha04a.pdf
    13 Feb 2023: Cells were collected in 300 µl ofRTL lysing solution (Qiagen) at the following times aftertreatment: 0, 2, 4, 6, 8, 18, 24, 32, 48, 72 h. ... Thecells used in this experiment were all expressing the T-cellreceptor (detected with anti CD3 antibodies) and
  3. Bayesian Gaussian Process Classificationwith the EM-EP…

    https://mlg.eng.cam.ac.uk/pub/pdf/KimGha06a.pdf
    13 Feb 2023: Itsgeneralized version which is convergent but slower hasbeen proposed [24]. 3.2 EP for Gaussian Process Classification. ... 00 0. CfJ. 24. 35; ð37Þ. where Cfj is a covariance matrix of latent values related to.
  4. btc654.tex

    https://mlg.eng.cam.ac.uk/pub/pdf/RavGhaWil02a.pdf
    13 Feb 2023: 0 226 0 98 310 0 14 322 0 214 24 0 1 23 0 2 4 0 21 16 0 9 24 0 115 205 51 20 265 0 11 ... 0 36 20 0 32 17 0 3518 28 23 0 28 0 23 23 0 28 27 0 24 28 0 2319 22 10 8 32 0 8 30 0
  5. uai2006.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/Wood-UAI-2006.pdf
    27 Jan 2023: The mean num-ber of signs per patient was 8.24 and the mean numberof stroke localizations was 1.96.
  6. MCMC for doubly-intractable distributions Iain MurrayGatsby…

    https://mlg.eng.cam.ac.uk/zoubin/papers/doubly_intractable.pdf
    27 Jan 2023: K. k=0. fk1(xk; θ, θ′)fk(xk; θ, θ′). (24)5. Draw r Uniform[0, 1]6.
  7. Approximate inference for the loss-calibrated Bayesian

    https://mlg.eng.cam.ac.uk/pub/pdf/LacHusGha11.pdf
    13 Feb 2023: p(θ) = N(θ|0,K1DD) (23). p(y|x,θ) = Φ(yKxDθ. σx. ), (24). where σ2x is as in (18), but with σ2 = 1.
  8. LNAI 3944 - Evaluating Predictive Uncertainty Challenge

    https://mlg.eng.cam.ac.uk/pub/pdf/QuiRasSinetal06.pdf
    13 Feb 2023: 101. 100. Outaouais (regression). NLPD. nMSE. (c). 0.22 0.24 0.26 0.28 0.30.2.
  9. chu05a.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/ChuGha05a.pdf
    13 Feb 2023: 25.732.24% 23.781.85% 23.751.74% 0.25960.0230 0.24110.0189 0.24110.0186Boston 25.561.98% 24.882.02% 24.491.85% 0.26720.0190
  10. vietri.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/vietri.pdf
    27 Jan 2023: Wewill deal exclusively with directed graphical models in this paper.4. texts [41, 24, 19] for details.Assume we observe some evidence: the value of some variables in the network.The ... If the parents of n are fp1; : : :;pkg and thechilden of n are fc1;
  11. The Supervised IBP: Neighbourhood PreservingInfinite Latent Feature…

    https://mlg.eng.cam.ac.uk/pub/pdf/QuaShaKnoGha13.pdf
    13 Feb 2023: 15 NN 31.52.6 27.82.8 28.13.2 35.51.0 44.52.1 39.33.730 NN 29.53.2 24.33.0 23.63.4
  12. standalone.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/QuiRasWil07.pdf
    13 Feb 2023: the approximateprior variances are replaced by the true prior variances. The predictive distribution of the FITC is identical to that of PITC (24),except for the alternative definition of Λ =
  13. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0910/lect01.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 1: Introduction to Machine Learning January 14th, 2010 24 / 26.
  14. - Machine Learning 4F13, Spring 2014

    https://mlg.eng.cam.ac.uk/teaching/4f13/1314/lect0102.pdf
    19 Nov 2023: 4. 3. 2. 1. 0. 1. 2. 3. Rasmussen and Ghahramani Lecture 1 and 2: Probabilistic Regression 24 / 36.
  15. FAST ONLINE ANOMALY DETECTION USING SCAN STATISTICS Ryan Turner ...

    https://mlg.eng.cam.ac.uk/pub/pdf/TurBotGha10.pdf
    13 Feb 2023: λ̂(t t) =Ni=1. ueu(t+tti) = eutNi=1. ueu(tti). = eutλ̂(t). (24). If a new event has occurred at t we must add k(0) at theend: ... Note that as t these equations approachthose without edge correction, (24), as expected.
  16. A robust Bayesian two-sample test for detecting intervals of ...

    https://mlg.eng.cam.ac.uk/pub/pdf/SteDenWiletal09.pdf
    13 Feb 2023: a total of 24 time points). B.cinerea spores (suspended in half-strength grape juice) germinate, penetrate theleaf and cause expanding necrotic lesions. ... This model is related to mixtures of Gaussian process experts, whichhave been studied previously
  17. - Machine Learning 4F13, Spring 2015

    https://mlg.eng.cam.ac.uk/teaching/4f13/1415/lect0102.pdf
    19 Nov 2023: 1. 0. 1. 2. 3. Samples from the posteriorRasmussen and Ghahramani Lecture 1 and 2: Probabilistic Regression 24 / 37.
  18. Gaussian Process

    https://mlg.eng.cam.ac.uk/teaching/4f13/1617/gaussian%20process.pdf
    19 Nov 2023: 64. 20. 24. 6. 6. 4. 2. 0. 2. 4. 6.
  19. - Machine Learning 4F13, Michaelmas 2015

    https://mlg.eng.cam.ac.uk/teaching/4f13/1516/lect0102.pdf
    19 Nov 2023: 1. 0. 1. 2. 3. Samples from the posteriorGhahramani Lecture 1 and 2: Probabilistic Regression 24 / 38.
  20. zglactive.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/zglactive.pdf
    27 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
  21. Continuous Relaxations for Discrete Hamiltonian Monte Carlo

    https://mlg.eng.cam.ac.uk/pub/pdf/ZhaSutSto12a.pdf
    13 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.
  22. The Infinite Hidden Markov Model Matthew J. Beal Zoubin ...

    https://mlg.eng.cam.ac.uk/zoubin/papers/ihmm.pdf
    27 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
  23. Sequential Decisions

    https://mlg.eng.cam.ac.uk/zoubin/SALD/week13sequential.pdf
    27 Jan 2023: solutions – the latter relating to “improper” priors! 24. Appendix: Background on the Von Neumann - Morgenstern theory of cardinal.
  24. Bayesian Hierarchical Clustering Katherine A. Heller…

    https://mlg.eng.cam.ac.uk/zoubin/papers/icml05heller.pdf
    27 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.
  25. paper.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/RotVanMooGha10.pdf
    13 Feb 2023: 0.99 1 0.77Soft-ss 0 1 0.96 0.99 0.03 0.21NBK 0.24 0.95 0.1 0.89 0.35 0.12.
  26. Reinforcement Learning with Reference Tracking Controlin Continuous…

    https://mlg.eng.cam.ac.uk/pub/pdf/HalRasMac11.pdf
    13 Feb 2023: 3] M. P. Deisenroth. Efficient Reinforcement Learning using GaussianProcesses. PhD thesis, Cambridge University, November 24 2009.
  27. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0708/lect01.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 1: Introduction to Machine Learning January 18th, 2008 24 / 26.
  28. Graphical models: parameter learning Zoubin Ghahramani Gatsby…

    https://mlg.eng.cam.ac.uk/zoubin/papers/graphical-models02.pdf
    27 Jan 2023: ar(x)p(x) = hr, (24). where r indexes the constraint. If the prior is set to the uniform distribution, and the constraints are measured.
  29. The infinite HMM for unsupervised PoS tagging Jurgen Van ...

    https://mlg.eng.cam.ac.uk/pub/pdf/VanVlaGha09.pdf
    13 Feb 2023: tions from our evaluation, which leaves us with 19sections instead of 24.
  30. Spectral Methods for Automatic Multiscale Data Clustering Arik…

    https://mlg.eng.cam.ac.uk/zoubin/papers/AzrGhaCVPR06.pdf
    27 Jan 2023: 24. S31. S32. Figure 5. Numerical demonstration of Algorithm 4. Data S consists of 9 words arranged on 3 lines.
  31. Generalization to Local Remappings of the VisuomotorCoordinate…

    https://mlg.eng.cam.ac.uk/zoubin/papers/genJN.pdf
    27 Jan 2023: Generalization to Local Remappings of the VisuomotorCoordinate Transformation. Zoubin Ghahramani,1 Daniel M. Wolpert,2 and Michael I. Jordan1. 1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge,
  32. SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases

    https://mlg.eng.cam.ac.uk/pub/pdf/LacPalDav13a.pdf
    13 Feb 2023: Finally, we mention thatPeralta [24] aligned the movie database MovieLens to IMDbthrough a combination of steps of manual cleaning with someautomation.
  33. chaptertr.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/advmf.pdf
    27 Jan 2023: ompute [24, 37, 13, 11, 10. ... Te hni al report, Cavendish Laboratory,University of Cambridge, 1997.[24 R. M.
  34. Probabilistic inference in graphical models Michael I.…

    https://mlg.eng.cam.ac.uk/zoubin/course04/hbtnn2e-I.pdf
    27 Jan 2023: links, see the articles in Jordan (1999). Jordan and Weiss: Probabilistic inference in graphical models 24.
  35. Gaussian Process

    https://mlg.eng.cam.ac.uk/teaching/4f13/2324/gaussian%20process.pdf
    19 Nov 2023: 64. 20. 24. 6. 6. 4. 2. 0. 2. 4. 6.
  36. 3F3: Signal and Pattern Processing Lecture 5: Dimensionality…

    https://mlg.eng.cam.ac.uk/teaching/3f3/1011/lect5.pdf
    19 Nov 2023: Dataset Data dim. Sample size MLE Regression Corr. dim.Swiss roll 3 1000 2.1(0.02) 1.8(0.03) 2.0(0.24)Faces 64 64 698 4.3
  37. Gaussian Processes — a brief introduction

    https://mlg.eng.cam.ac.uk/teaching/4f13/2324/gp.pdf
    19 Nov 2023: 64. 20. 24. 6. 6. 4. 2. 0. 2. 4. 6. ... Rasmussen Gaussian Processes October 23th, 2023 24 / 27. 43. 21.
  38. ICML-Presentation

    https://mlg.eng.cam.ac.uk/zoubin/talks/ICML-Presentation.pdf
    27 Jan 2023: International Conference onMachine Learning. Corvallis, Oregon, June 20-24 2007. Summary Presentation:Statistics, Awards, Comments.
  39. t.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/MinGha03.pdf
    27 Jan 2023: HG e! lk! (24). wherek ] a? b i $W BA (25)k! ]!
  40. Scalingin a Hierar chical Unsupervised Network 1 Zoubin Ghahramani,2…

    https://mlg.eng.cam.ac.uk/pub/pdf/GhaKorHin99a.pdf
    13 Feb 2023: Eachof the 24 hiddenunits in the middle hiddenlayerwas connectedto 9 consecutive visible units from eacheye,i.e. ... e), 1-24-36(b,f),1-48-72(c,g), 1-72-108(d,h).
  41. linsys-new.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/tr-96-2.pdf
    27 Jan 2023: Initial state covariance:@Q@V 11 = 12V1 12(P1 x̂101 1x̂01 101) (23)V new1 = P1 x̂1x̂01 (24)The above equations can be readily generalized to multiple observation sequences, withone subtlety
  42. paper.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/RotVanMooetal10.pdf
    13 Feb 2023: 0.99 1 0.77Soft-ss 0 1 0.96 0.99 0.03 0.21NBK 0.24 0.95 0.1 0.89 0.35 0.12.
  43. A Nonparametric Bayesian Model for Multiple Clustering…

    https://mlg.eng.cam.ac.uk/pub/pdf/NiuDyGha12.pdf
    13 Feb 2023: 65CRP-CRP 0.87 0.66 0.34 0.87DP-Gauss 0.24 0.27 0.23 0.016. ... on Data Mining,pages 530–539, 2008. [24] A. Strehl and J. Ghosh.
  44. A Systematic Bayesian Treatment of the IBM Alignment Models ...

    https://mlg.eng.cam.ac.uk/pub/pdf/GalBlu13.pdf
    13 Feb 2023: HMM Model Model 420. 21. 22. 23. 24. 25. 26. 27.
  45. newroyftp.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/HinGha97a.pdf
    13 Feb 2023: A factor analyzer with 24 hidden units discoversglobal features with both excitatory and inhibitory components (gure 9a). ... a) Weights from the top layer hidden unit to the 24 middle-layer hidden units.
  46. Formatting Instructions for NIPS -8-

    https://mlg.eng.cam.ac.uk/zoubin/papers/JinGha02.pdf
    27 Jan 2023: Class Name ecoli wine pendigit iris glass. Naive 17.3% 10% 14.2% 18.5% 24.9% 1 extra label by random distracter EM 13.6% 4.4% 8.9%
  47. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0809/lect01.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 1: Introduction to Machine Learning January 16th, 2009 24 / 26.
  48. analogy-aistats2007.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/SilHelGha07a.pdf
    13 Feb 2023: C1 C2 RB SB1 SB2 C1 C2 RB SB1 SB2. student course f aculty projectcornell 0.87 0.61 0.87 0.84 0.80 0.19 0.04 0.24 ... 0.18 0.18texas 0.55 0.54 0.77 0.62 0.48 0.24 0.07 0.29 0.07 0.12.
  49. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0708/lect07.pdf
    19 Nov 2023: But we can not usually simulate Hamiltonian dynamics exactly. Ghahramani & Rasmussen (CUED) Lecture 7: Markov Chain Monte Carlo February 8th and 13th, 2008 24 / 28.
  50. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0708/lect04.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 4: Graphical Models January 30th, 2008 24 / 1.
  51. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0809/lect04.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 4: Graphical Models January 27th, 2009 24 / 25.

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