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Bayesian Knowledge Corroboration with LogicalRules and User Feedback…
https://mlg.eng.cam.ac.uk/pub/pdf/KasVanGraHer10.pdf13 Feb 2023: While [23, 24] represent the formalism of first-orderlogic by factor graph models, [27] and [29] deal with Bayesian networks appliedto first-order logic. ... 1094–1099. AAAI Press(2008). 24. Domingos, P., Richardson, M.: Markov Logic Networks. -
Nonparametric Transforms of Graph Kernelsfor Semi-Supervised Learning …
https://mlg.eng.cam.ac.uk/zoubin/papers/ZhuKanGhaLaf04.pdf27 Jan 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 -
1 Robust Filtering and Smoothing with Gaussian Processes Marc ...
https://mlg.eng.cam.ac.uk/pub/pdf/DeiTurHubetal12.pdf13 Feb 2023: Using the definition of S in (24), the productof the two Gaussians in (36) results in a new (unnormalized) Gaussianc14 N(xt1 |ψi, Ψ) with. ... 24] J. Kocijan, R. Murray-Smith, C. E. Rasmussen, and B. Likar, “PredictiveControl with Gaussian Process -
Bayesian Sets Zoubin Ghahramani∗ and Katherine A. HellerGatsby…
https://mlg.eng.cam.ac.uk/zoubin/papers/bsets-nips05.pdf27 Jan 2023: Behavioral and Brain. Sciences, 24:629–641.[6] Tong, S. (2005). Personal communication. -
images/test_user_webdesign.eps
https://mlg.eng.cam.ac.uk/pub/pdf/IwaShaGha13a.pdf13 Feb 2023: We estimate the Dirichlet parameter β by using the fixed-point iteration method described in [24]. ... γW. EnX. n′=1. αun exp(γtn)! , (24). where W () is the Lambert W function, which solves theequation x = W (x) exp(W (x)), and. -
Prediction at an Uncertain Input for GaussianProcesses and Relevance…
https://mlg.eng.cam.ac.uk/pub/pdf/QuiGirRas03.pdf13 Feb 2023: Ex[σ2(x)] varx(µ(x. )) (24). where Ex indicates the expectation under x. -
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. -
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 -
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]. -
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 -
Machine Learning Group Publications
https://mlg.eng.cam.ac.uk/pub/topics/13 Feb 2023: Publications, Machine Learning Group, Department of Engineering, Cambridge. current group:. [former members:. [by year:. [Gaussian Processes and Kernel Methods. Gaussian processes are non-parametric distributions useful for doing Bayesian inference -
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). -
Gaussian Process
https://mlg.eng.cam.ac.uk/teaching/4f13/1718/gaussian%20process.pdf19 Nov 2023: 64. 20. 24. 6. 6. 4. 2. 0. 2. 4. 6. -
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 -
MCMC for doubly-intractable distributions Iain MurrayGatsby…
https://mlg.eng.cam.ac.uk/pub/pdf/MurGhaMac06a.pdf13 Feb 2023: K. k=0. fk1(xk; θ, θ′)fk(xk; θ, θ′). (24)5. Draw r Uniform[0, 1]6. -
Bayesian Learning in Undirected Graphical Models:Approximate MCMC…
https://mlg.eng.cam.ac.uk/zoubin/papers/uai04murray.pdf27 Jan 2023: Pattern Recogni-tion Letters, 24:1251–1259, 2003. [19] David Edwards and Tomáš Havránek. -
aistats2007-ibpsb.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/TehGorGha07a.pdf13 Feb 2023: Givens, the distribution ofZ becomes:. p(Z|x, s, µ(1:)) p(Z|x, µ(1:))1. µI(0sµ) (24). -
uai2006.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/WooGriGha06a.pdf13 Feb 2023: The mean num-ber of signs per patient was 8.24 and the mean numberof stroke localizations was 1.96. -
Graphical models: parameter learning Zoubin Ghahramani Gatsby…
https://mlg.eng.cam.ac.uk/zoubin/course04/hbtnn2e-II.pdf27 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. -
Graphical models: parameter learning Zoubin Ghahramani Gatsby…
https://mlg.eng.cam.ac.uk/zoubin/course03/hbtnn2e-II.pdf27 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. -
A Kernel Approach to Tractable Bayesian Nonparametrics
https://mlg.eng.cam.ac.uk/pub/pdf/HusLac11.pdf13 Feb 2023: method novelty(AuC[%]) recons.(conf[%]). kst 96.82 0.8 2.15 1.0kde 94.56 0.6 9.24 1.9dpm 73.56 4.3 23.85 8.2. -
Choosing a Variable to Clamp:Approximate Inference Using Conditioned…
https://mlg.eng.cam.ac.uk/pub/pdf/EatGha09.pdf13 Feb 2023: xα)d/m̃αi(xi) (24). -
analogy-aistats2007.dvi
https://mlg.eng.cam.ac.uk/zoubin/papers/analogy-aistats2007.pdf27 Jan 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. -
1471-2105-10-242.fm
https://mlg.eng.cam.ac.uk/pub/pdf/SavHelXuetal09.pdf13 Feb 2023: 24], a set of 997 mRNA profilesacross 20 experiments representing systematic perturba-tions of the yeast galactose-utilization pathway. ... Genome Biol 2003,4(5):R34. 24. Ideker T, Thorsson V, Ranish J, Christmas R, Buhler J, Eng J, Bumgar-ner R, -
1212 nature neuroscience supplement • volume 3 • november ...
https://mlg.eng.cam.ac.uk/zoubin/papers/natneuro.pdf27 Jan 2023: 24. Todorov, E. Direct cortical control of muscle activation in voluntary armmovements: a model. -
/users/joe/src/tops/dvips
https://mlg.eng.cam.ac.uk/pub/pdf/UedNakGha00a.pdf13 Feb 2023: Table 1: Log-Likelihood/Sample Size. Initial value EM DAEM SMEM. Training mean 159.1 148.2 147.9 145.1std 1.77 0.24 0.04 0.08max 157.3 -
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
https://mlg.eng.cam.ac.uk/pub/pdf/ParRasPetDoy18.pdf13 Feb 2023: 24. 26. 28. 30. Particle value estimateMoment matching value estimate. Distance in parameter space ( ). Val. ue. (a). -0.15 -0.1 -0.053.3. 3.35. 3.4. 3.45. ... COST FUNCTION NOISE MULTIPLIER PILCO RP RPFS GR GRFS LR TP TPσf TPσnANGLE COST k = 102 0.88 0 -
Compact approximations to Bayesian predictive distributions Edward…
https://mlg.eng.cam.ac.uk/zoubin/papers/icml05snelson.pdf27 Jan 2023: 24 data points were generated, half labeled 1,the half other 1; these points are shown as circles andcrosses in Figure 2. -
Gaussian Processes for time-marked time-series data John P.…
https://mlg.eng.cam.ac.uk/pub/pdf/CunGhaRas12.pdf13 Feb 2023: 2010) (neuron 184, con-dition 24), and firing rates were calculated by smooth-ing the point process data with a Gaussian kernel ofσ = 26ms, which is conventional for neural firing rateanalysis. -
gppl.dvi
https://mlg.eng.cam.ac.uk/zoubin/papers/icml05chuwei-pl.pdf27 Jan 2023: f MAPa = Σaβa (24). where βa =. ni=1. gij=1 ln Φ(z. ... de-fined as in (24) at θ. -
aistats2007-ibpsb.dvi
https://mlg.eng.cam.ac.uk/zoubin/papers/TehGorGha07.pdf27 Jan 2023: Givens, the distribution ofZ becomes:. p(Z|x, s, µ(1:)) p(Z|x, µ(1:))1. µI(0sµ) (24). -
Bayesian Structured Prediction using Gaussian Processes Sébastien…
https://mlg.eng.cam.ac.uk/pub/pdf/BraQuaGha14a.pdf13 Feb 2023: all 20 sessions, the error rates were 52.12 11.73 for the CRF, 51.91 11.02for GPstruct linear kernel, and 50.42 11.24 for GPstruct SE kernel. -
Bayesian correlated clustering to integrate multiple datasetsPaul…
https://mlg.eng.cam.ac.uk/pub/pdf/KirGriSavGhaetal12.pdf13 Feb 2023: Expression 7.66 1.15 9.48 551ChIPPPI 27.04 3.47 18.99 31. ChIPExpression 24.46 2.93 16.87 48PPIExpression 26.04 3.69 22.35 32. -
ency02.dvi
https://mlg.eng.cam.ac.uk/zoubin/course04/hbtnn2e-III.pdf27 Jan 2023: 5 454 9 44 5 312 14 47 8 216 20 35 13 96 28 24. -
ency02.dvi
https://mlg.eng.cam.ac.uk/zoubin/course03/hbtnn2e-III.pdf27 Jan 2023: 5 454 9 44 5 312 14 47 8 216 20 35 13 96 28 24. -
TCS November 2001, 2nd pages.qxd
https://mlg.eng.cam.ac.uk/zoubin/papers/WolGhaFla01.pdf27 Jan 2023: J. Math. Biol. 15,267–273. 24 Linsker, R. (1986) From basic network principles toneural architecture: emergence of spatial-opponentcells. -
Probabilistic inference in graphical models Michael I.…
https://mlg.eng.cam.ac.uk/zoubin/course03/hbtnn2e-I.pdf27 Jan 2023: links, see the articles in Jordan (1999). Jordan and Weiss: Probabilistic inference in graphical models 24.
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