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151 - 200 of 261 search results for KaKaoTalk:PC53 24 / |u:mlg.eng.cam.ac.uk where 0 match all words and 261 match some words.

  1. Results that match 1 of 2 words

  2. - 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.

  3. - 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.

  4. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0910/lect04.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 4: Graphical Models January 27th, 2010 24 / 25.

  5. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0910/lect07.pdf
    19 Nov 2023: But we can not usually simulate Hamiltonian dynamics exactly. Ghahramani & Rasmussen (CUED) Lecture 7 and 8: Markov Chain Monte Carlo February 10th and 11th, 2010 24 / 28.

  6. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0809/lect07.pdf
    19 Nov 2023: But we can not usually simulate Hamiltonian dynamics exactly. Ghahramani & Rasmussen (CUED) Lecture 7 and 8: Markov Chain Monte Carlo February 6th and 10th, 2009 24 / 28.

  7. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect04.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 4: Graphical Models 24 / 26.

  8. Bayesian Learning of Model Structure Zoubin GhahramaniGatsb y…

    https://mlg.eng.cam.ac.uk/zoubin/talks/cmu-talk.pdf
    27 Jan 2023: 4 5 3 5 3 5 4 3. 24. 34. 33. ... 32. 24. 45. 54. 35. 55. 34. 44. 44. 4. 35.

  9. Marc P. Deisenroth, Carl E. Rasmussen, and Jan Peters: ...

    https://mlg.eng.cam.ac.uk/pub/pdf/DeiRasPet08.pdf
    13 Feb 2023: pages 19–24, Bruges, Belgium, April 2008. Model-Based Reinforcement Learning withContinuous States and Actions.

  10. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1213/lect0304.pdf
    19 Nov 2023: 1. 2M=0. 1 0 1 24. 2. 0. 2. 4. M=1. ... p(x, y)dy = p(x):. wkp(w)dw =. wk(. p(wk, w/k)dw/k. )dwk =. wkp(wk)dwk. Rasmussen & Ghahramani (CUED) Lecture 3 and 4: Gaussian Processes 24 / 32.

  11. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1112/lect0304.pdf
    19 Nov 2023: 1. 2M=0. 1 0 1 24. 2. 0. 2. 4. M=1. ... p(x, y)dy = p(x):. wkp(w)dw =. wk(. p(wk, w/k)dw/k. )dwk =. wkp(wk)dwk. Quiñonero-Candela & Rasmussen (CUED) Lecture 3 and 4: Gaussian Processes 24 /

  12. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0708/lect02.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 2, 3: PCA, FA and EM January 23rd, 25th, 2008 24 / 27.

  13. Computational structure of coordinatetransformations: A…

    https://mlg.eng.cam.ac.uk/zoubin/papers/coord.pdf
    27 Jan 2023: 24{1{24{45.Wiley{Interscience, New York.

  14. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0809/lect0203.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 2, 3: PCA, FA and EM January 20th, 23rd, 2009 24 / 27.

  15. chu.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/ChuGhaKra06a.pdf
    13 Feb 2023: MALDI data). Inspection of the normalized von Neu-mann diffusion kernel for this data (Figure 4, bottom right) indicated thata subset of this data (24 baits and 49 proteins) formed clear ... 2 3 12 14 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

  16. A Bayesian Approach to Modeling Uncertainty inGene Expression…

    https://mlg.eng.cam.ac.uk/zoubin/papers/icsb2002_full.pdf
    27 Jan 2023: Nature Genetics,24(3):236–244, 2000.

  17. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect1214.pdf
    19 Nov 2023: knowledge of transition probabilities and rewards• exploration vs. exploitation. Ghahramani & Rasmussen (CUED) Lecture 12, 13, 14: Reinforcement Learning 24 / 25.

  18. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect05.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 5: Graphical Models: Inference 24 / 31.

  19. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0809/lect05.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 5: Graphical Models: Inference January 30th, 2009 24 / 31.

  20. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0708/lect05.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 5: Graphical Models: Inference February 1st, 2008 24 / 31.

  21. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0910/lect14.pdf
    19 Nov 2023: knowledge of transition probabilities and rewards• exploration vs. exploitation. Ghahramani & Rasmussen (CUED) Lecture 14, 15, 16: Reinforcement Learning March 3rd, 4th and 10th, 2010 24 / 25.

  22. nips.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/Ras96.pdf
    13 Feb 2023: 0.2. 0.3. 0.4. 0.5. 0.6. House. 24 48 96 1920. 0.05.

  23. WolGha05 handout

    https://mlg.eng.cam.ac.uk/zoubin/papers/WolGha06.pdf
    27 Jan 2023: L. Generalization, similarity, and Bayesian inference. Behav Brain Sci 24,. 629-40; discussion 652-791 (2001).

  24. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0910/lect05.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 5: Graphical Models: Inference January 28th, 2010 24 / 31.

  25. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0809/lect13.pdf
    19 Nov 2023: knowledge of transition probabilities and rewards• exploration vs. exploitation. Ghahramani & Rasmussen (CUED) Lecture 13, 14, 15: Reinforcement Learning February 27th, March 3rd and 6th, 2009 24 / 25.

  26. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0708/lect13.pdf
    19 Nov 2023: knowledge of transition probabilities and rewards• exploration vs. exploitation. Ghahramani & Rasmussen (CUED) Lecture 13, 14, 15: Reinforcement Learning February 29th, March 5th and 7th, 2008 24 / 25.

  27. Scalable Gaussian Process Structured Prediction for Grid Factor Graph …

    https://mlg.eng.cam.ac.uk/pub/pdf/BraQuaNowGha14.pdf
    13 Feb 2023: 24.6. 24.7. 24.8. 24.9. 25.0err. or. rate. GPstruct. CRF LBMO bag. ... 2013. http://arxiv.org/abs/1307.3846. Breiman, Leo. Bagging predictors. Machine Learning, 24(2):123–140, 1996. Domke, Justin.

  28. Inferring a measure of physiological age frommultiple ageing related…

    https://mlg.eng.cam.ac.uk/pub/pdf/KnoParGlaWin11.pdf
    13 Feb 2023: 2. Figure 1: An intuitive explanation of our model. This 55 year old individual has the cataracts of a79 year old, implying = 24 years.

  29. erice.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/erice.pdf
    27 Jan 2023: d) Weights from the toplayer binary logistic unit to the 24 middle layer binary logistic units. ... a) Weights from the top layer linear-Gaussian unit tothe 24 middle layer linear-Gaussian units.

  30. G:\bioinformatics\Bioinfo-26(7)issue\btq053.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/LipGhaBor10.pdf
    13 Feb 2023: 915. at Cam. bridge University Library on July 24, 2010. http://bioinformatics.oxfordjournals.org. ... 916. at Cam. bridge University Library on July 24, 2010. http://bioinformatics.oxfordjournals.org.

  31. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect0203.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 2, 3: PCA, FA and EM 24 / 32.

  32. Communicated by David MacKay Pruning from Adaptive Regularization…

    https://mlg.eng.cam.ac.uk/pub/pdf/HanRas94.pdf
    13 Feb 2023: Neural Syst. 1, 317-326. Received May 14,1993; accepted January 24, 1994.

  33. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0910/lect0203.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 2, 3: PCA, FA and EM January 20th, 21st 2010 24 / 32.

  34. The Rendezvous Algorithm: Multiclass Semi-Supervised Learningwith…

    https://mlg.eng.cam.ac.uk/pub/pdf/Azr07.pdf
    13 Feb 2023: Low Density Separation (Chapelle. Appearing in Proceedings of the 24 th International Confer-ence on Machine Learning, Corvallis, OR, 2007. ... 42.72 13.49 4.95 3.79 9.68 21.99 35.17 24.38Data dep.

  35. AA06.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/GirRasQuiMur03.pdf
    13 Feb 2023: 2. 22 24 26 28 30 32 346. 4. 2. 0.

  36. paperftp.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/modul.pdf
    27 Jan 2023: As in previous studies of the visuomo-tor system [23, 24, 25], the internal structure of thesystem can be probed by investigating the generaliza-tion properties in response to novel inputs, ... Constraints on learning new mappingsbetween perceptual

  37. Dirichlet Process Mixture Models for Verb Clustering Andreas Vlachos…

    https://mlg.eng.cam.ac.uk/pub/pdf/VlaGhaKor08.pdf
    13 Feb 2023: gauss 78.54% 50.22% 61.26%34 classes. vanilla 70.24% 78.94% 74.34%link34 100 73.19% 79.24& 76.10%.

  38. SMEM Algorithm for Mixture Models

    https://mlg.eng.cam.ac.uk/pub/pdf/UedNakGha98a.pdf
    13 Feb 2023: initiall value EM DAEM. mean -159.1 -148.2 -147.9 Training std 1.n 0.24 0.04 data.

  39. The Infinite Partially Observable Markov DecisionProcess Finale…

    https://mlg.eng.cam.ac.uk/pub/pdf/Dos09b.pdf
    13 Feb 2023: 24, pp. 195–220, 2005. [14] T. Smith and R. Simmons, “Heuristic search value iteration for POMDPs,” inProc. ... 24] J. H. Robert, R. St-aubin, A. Hu, and C. Boutilier, “SPUDD: Stochastic planning using decisiondiagrams,” inUAI, pp.

  40. PROPAGATION OF UNCERTAINTY IN BAYESIAN KERNEL MODELS— APPLICATION TO…

    https://mlg.eng.cam.ac.uk/pub/pdf/QuiGirLarRas03.pdf
    13 Feb 2023: Lij = ki(u)kj (u) |2Λ1S I|12 (24). exp[2(u xd)>Λ1(2Λ1 S1)1Λ1(u xd). ],.

  41. Time-Sensitive Dirichlet Process Mixture Models Xiaojin Zhu Zoubin…

    https://mlg.eng.cam.ac.uk/zoubin/papers/tdpmTR.pdf
    27 Jan 2023: w(t, c) =. i:ti<t,si=c. k(t ti) =. eλ(tti) (24). λw(t, c) =. i:ti<t,si=c. (t ti)eλ(tti) (25). We then take a

  42. LNCS 5342 - Outlier Robust Gaussian Process Classification

    https://mlg.eng.cam.ac.uk/pub/pdf/KimGha08a.pdf
    13 Feb 2023: label-change rate(%) 0 5 10 15SVM error(%) 4.070.60 5.090.96 6.761.10 8.801.13GPC log-ev -24.40.6 -41.80.8 -51.81.1 ... MS-robust- log-ev -24.40.6 -41.00.7 -50.50.7 -58.60.8GPC error(%) 3.700.36 4.540.62 6.760.76 6.850.73.

  43. MODEL BASED LEARNING OF SIGMA POINTS IN UNSCENTED KALMAN ...

    https://mlg.eng.cam.ac.uk/pub/pdf/TurRas10.pdf
    13 Feb 2023: 2.24 0.369 N/A 3.60 0.477 N/A 1.05 0.0692 N/AEKF 617554 0.0149 9.690.977 <0.0001 1.750.113

  44. Eurocon_final.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/KocMurRasLik03.pdf
    13 Feb 2023: More on this topiccan be found in [7], [24].Linear MPC It is worth to remark that even though this is a con-strained nonlinear MPC problem it can be used ... 24] Zheng A., Morari M., Stability of model predictive control with mixedconstraints, IEEE Trans.

  45. Encyclopedia of Cognitive Science—Author Stylesheet ©Copyright…

    https://mlg.eng.cam.ac.uk/zoubin/papers/ECS-infotheory02.pdf
    27 Jan 2023: After the neighbour tells you that he lives on the top floor, the probability of X drops to 0 for 24 of the 32 values and becomes 1/8 for the

  46. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1213/lect0102.pdf
    19 Nov 2023: p(y|x, M). Rasmussen & Ghahramani (CUED) Lecture 1 and 2: Probabilistic Regression 24 / 32.

  47. grasshopper.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/GolZhuVanAnd07.pdf
    13 Feb 2023: Afterexamining the results for all 24 configurations, weselected the best one:α = 0.25 andλ = 0.5. ... of 11DUC 2004 Task 4b 24 0.4067 [0.3883, 0.4251] Between 2 & 3 of 11.

  48. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1112/lect0102.pdf
    19 Nov 2023: p(y|x, M). Quiñonero-Candela & Rasmussen (CUED) Lecture 1 and 2: Probabilistic Regression 24 / 32.

  49. main.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/Sch09b.pdf
    13 Feb 2023: Qzz qz (24)[Qz ]. i:. ︸ ︷︷ ︸. d. zi qz [Qz]ĩ: zĩ︸ ︷︷ ︸.

  50. o407_12f 742..747

    https://mlg.eng.cam.ac.uk/zoubin/papers/reza.pdf
    27 Jan 2023: Received 24 March; accepted 31 July 2000. 1. Darwin, C. The Origin of Species by Means of Natural Selection (Murray, London, 1859). ... 24. Amirikian, B. & Georgopulos, A. P. Directional tuning profiles of motor cortical cells.

  51. book

    https://mlg.eng.cam.ac.uk/zoubin/papers/CGM.pdf
    27 Jan 2023: 1.23). subject to:. wt φt(xn, ynt) wt φt(xn, yt) Mynt,yt ξnt n, t, yt (1.24). ... We end up having a significant reduction in the number ofconstraints2 in our optimisation formulation for CGMs in (1.23)-(1.24).

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