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A Nonparametric Bayesian Model for Multiple Clustering…
https://mlg.eng.cam.ac.uk/pub/pdf/NiuDyGha12.pdf13 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. -
newroyftp.dvi
https://mlg.eng.cam.ac.uk/zoubin/papers/RGBN.pdf27 Jan 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. -
Nonparametric Bayesian Sparse Factor Models with application to Gene…
https://mlg.eng.cam.ac.uk/pub/pdf/KnoGha11b.pdf13 Feb 2023: 14 15 16 17 18 19 20 21 22 23 24 25 26 270. ... 2004) (N = 24,D = 100,3000 MCMC iterations). 5.3. Biological data: E. -
Student-t Processes as Alternatives to Gaussian Processes Amar Shah…
https://mlg.eng.cam.ac.uk/pub/pdf/ShaWilGha14a.pdf13 Feb 2023: 40. 20. 0. 20. Function Evaluation. Max. FunctionValue. GPTP. 1. 4 8 12 16 20 24 28 32 36 400. ... 5. 10. 15. 20. Function Evaluation. Min. FunctionValue. GPTP. 1. 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30. -
Latent-Space Variational Bayes Jaemo Sung, Student Member,…
https://mlg.eng.cam.ac.uk/pub/pdf/SunGhaBan08.pdf13 Feb 2023: 4 MIXTURE OF GAUSSIANS. Finite mixture [23], [24] is a latent variable model which provides a. ... been used to demonstrate inference algorithms inthe pattern recognition, statistics, and machine learning literatures[6], [24], [25], [26]. -
Graph-based Semi-supervised Learning Zoubin Ghahramani Department of…
https://mlg.eng.cam.ac.uk/zoubin/talks/lect3ssl.pdf27 Jan 2023: Background Extraction. date 10/24 11/13 1/6 1/14 1/20 1/21 1/271 128 193 153 4742 256 193 4483 288 305 5934 204 190 3945 266 -
368 A kernel method for unsupervised structured network inference ...
https://mlg.eng.cam.ac.uk/pub/pdf/LipSteGhaetal09.pdf13 Feb 2023: The Countries data set contains in-formation on 24 countries, comprising 4 numeric at-tributes: population size, the GNP per capita, theaverage education level (as expressed by scalars), aswell as energy ... Countries trade data (24 nodes)population size -
Predictive Automatic Relevance Determinationby Expectation…
https://mlg.eng.cam.ac.uk/zoubin/papers/Qi04.pdf27 Jan 2023: PredictiveARDEPPredictiveProbARDEP. EvidenceARDEPAllFeaturesEPlinear. SVMQP_RegAdaBoostLP_RegAdaBoost. AdaBoost_RegAdaBoost. RBF. 23.5 24.5 25.5 26.5. Test error rate. -
Accelerated Sampling for the Indian Buffet Process Finale Doshi-Velez …
https://mlg.eng.cam.ac.uk/pub/pdf/DosGha09.pdf13 Feb 2023: 24.557 -13.977 -26.936. The running times and quality criteria are summarisedin table 2. -
Predictive Automatic Relevance Determinationby Expectation…
https://mlg.eng.cam.ac.uk/pub/pdf/QiMinPic04a.pdf13 Feb 2023: PredictiveARDEPPredictiveProbARDEP. EvidenceARDEPAllFeaturesEPlinear. SVMQP_RegAdaBoostLP_RegAdaBoost. AdaBoost_RegAdaBoost. RBF. 23.5 24.5 25.5 26.5. Test error rate. -
Marc P. Deisenroth, Jan Peters, and Carl E. Rasmussen: ...
https://mlg.eng.cam.ac.uk/pub/pdf/DeiPetRas08.pdf13 Feb 2023: 578 2.0108 9.23 6.2105 9.81012 9.02488 1.2108 9.24 3.1105 1.31012 9.05. -
Gaussian Process Change Point Models
https://mlg.eng.cam.ac.uk/pub/pdf/SaaTurRas10.pdf13 Feb 2023: In FinancialTimes. April 24 2009. Li, D. X. On default correlation: A copula function ap-proach. -
Accelerated sampling for the Indian Buffet Process
https://mlg.eng.cam.ac.uk/pub/pdf/DosGha09a.pdf13 Feb 2023: 24.557 -13.977 -26.936. The running times and quality criteria are summarisedin table 2. -
Bayesian Learning in Undirected Graphical Models:Approximate MCMC…
https://mlg.eng.cam.ac.uk/pub/pdf/MurGha04a.pdf13 Feb 2023: Pattern Recogni-tion Letters, 24:1251–1259, 2003. [19] David Edwards and Tomáš Havránek. -
LNAI 8189 - Variational Hidden Conditional Random Fields with Coupled …
https://mlg.eng.cam.ac.uk/pub/pdf/BouZafMor13a.pdf13 Feb 2023: L(q) = 〈log F(y, x, π′, X)p(π′)〉q(y,s,π′|X) 〈log q(y, s|X)q(π′)〉q(y,s,π′|X)(24). Since log ... IEEE Trans. Neural Networks andLearning Systems 24(1), 170–177 (2013). 8. Ghahramani, Z., Beal, M.: Propagation Algorithms for -
Relational Learning with Gaussian Processes Wei ChuCCLS Columbia…
https://mlg.eng.cam.ac.uk/pub/pdf/ChuSinGhaetal07.pdf13 Feb 2023: 28. 26. 24. 22. 20. 18. κ. log P(E). log P(E,Y). -
Relational Learning with Gaussian Processes Wei ChuCCLS Columbia…
https://mlg.eng.cam.ac.uk/zoubin/papers/relationalgp.pdf27 Jan 2023: 28. 26. 24. 22. 20. 18. κ. log P(E). log P(E,Y). -
Variational Inference for the Indian Buffet Process Finale…
https://mlg.eng.cam.ac.uk/pub/pdf/DosMilVanTeh09.pdf13 Feb 2023: 25 2973.7 -2.2475 163.24 -1.066. Finite Variational 10 767.1 -0.90825 10072 -0.7465 176.62 -1.051. -
Learning to Control a Low-Cost Manipulator usingData-Efficient…
https://mlg.eng.cam.ac.uk/pub/pdf/DeiRasFox11.pdf13 Feb 2023: The idea is related to [24],where planning is performed with fully known dynamics anda piecewise linear controller. ... 24] M. Toussaint and C. Goerick. From Motor Learning toInteraction Learning in Robots, chapter A Bayesian Viewon Motor Control and -
OP-CBIO120293 3290..3297
https://mlg.eng.cam.ac.uk/pub/pdf/KirGriSav12a.pdf13 Feb 2023: Vol. 28 no. 24 2012, pages 3290–3297BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/bts595. Systems biology Advance Access publication October 9, 2012. ... Expression 7.66 1.15 9.48 551. ChIPþPPI 27.04 3.47 18.99 31ChIPþExpression 24.46 2.93
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