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520 Factorial Mixture of Gaussians andthe Marginal Independence Model …
https://mlg.eng.cam.ac.uk/pub/pdf/SilGha09b.pdf13 Feb 2023: The resulting predictive log-likelihood forthe five folds were -7.18, -7.15, -7.09, -7.24, -7.31 for thebi-directed model. ... For the LVM trained by maximumlikelihood and EM, the results were -10.78, -10.24, -9.68, -10.04, -10.28, showing a sizeable -
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 -
paper.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/RotVanMooGha10.pdf13 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. -
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. -
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]. -
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. -
Formatting Instructions for NIPS -8-
https://mlg.eng.cam.ac.uk/zoubin/papers/JinGha02.pdf27 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% -
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. -
https://mlg.eng.cam.ac.uk/blog/feed.xml
https://mlg.eng.cam.ac.uk/blog/feed.xml12 Apr 2024: Jekyll 2024-04-12T16:32:5900:00 https://mlg.eng.cam.ac.uk/blog/feed.xml MLG Blog Blog of the Machine Learning Group at the University of Cambridge An introduction to Flow Matching 2024-01-20T00:00:0000:00 2024-01-20T00:00:0000:00 -
Directed and Undirected Graphical Models
https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW1-models.pdf16 Jul 2024: 23 / 26. Directed and undirected models are different. 24 / 26. ... Directed and undirected models are different. With <3 edges,. 24 / 26. -
Junction Tree, BP and Variational Methods
https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW3-approx.pdf16 Jul 2024: 24 / 32. The log-partition function log Z. Since D(q‖p) 0, we have. ... a finite convex hull 3. M(G) = conv {φ(e), e X m} (5.24). -
Structured Prediction Models for Chord Transcription of Music Audio…
https://mlg.eng.cam.ac.uk/adrian/icmla09adrian.pdf16 Jul 2024: To its right, 0-1 adds the features of the previous frame, so in this model each frame is represented by 24 dimensions. -
paper.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/Gha01a.pdf13 Feb 2023: J> #. 6? 1. 'R. 4. &S. 2B. " S " ". R. 24. 6 1 ". 0 "Q ". " 1 " 2B 24. -
Bounding the Integrality Distance ofLP Relaxations for Structured…
https://mlg.eng.cam.ac.uk/adrian/OPT2016_paper_3.pdf16 Jul 2024: 24)Using the same approach to upper-bound Equation 22, we assume, without loss of generality, that(δ(µI ) θ) µ̃L (δ(µI ) θ. ′) ... m. (25). Using Equation 25 to upper-bound 22, and Equation 24 to upper-bound 23, we have that. -
Understanding the Bethe Approximation: When and How can it ...
https://mlg.eng.cam.ac.uk/adrian/abc.pdf16 Jul 2024: FA8750-14-C-0005. 2 8 16 24 320. 20. 40. 60. 80. 100. ... Maximum coupling strength y. BethelocalBethecycleBethemargTRWlocalTRWcycleTRWmarg. (a) log partition error. 2 8 16 24 320. -
Transparency: Motivations and Challenges? Adrian…
https://mlg.eng.cam.ac.uk/adrian/transparency.pdf16 Jul 2024: Notions of fairness beyond equality, and the role ofrandomness in fairness, were recently explored [24, 74]. ... Advances in experimental social psychology 24, 319–359 (1991). 26. Hammond, R., Axelrod, R.: The evolution of ethnocentrism. -
- Machine Learning 4F13, Michaelmas 2015
https://mlg.eng.cam.ac.uk/teaching/4f13/1516/lect1011.pdf19 Nov 2023: a probability? Ghahramani Lecture 10 and 11: Text and Discrete Distributions 10 / 24. ... Ghahramani Lecture 10 and 11: Text and Discrete Distributions 24 / 24. -
ML-IRL: Machine Learning in Real Life Workshop at ICLR ...
https://mlg.eng.cam.ac.uk/adrian/ML_IRL_2020-CLUE.pdf16 Jul 2024: 6). LSATAl. (7). COMPASEp. (6). COMPASAl. (5) Total (24). Prolific Unc. -
- Machine Learning 4F13, Spring 2015
https://mlg.eng.cam.ac.uk/teaching/4f13/1415/lect1011.pdf19 Nov 2023: Rasmussen and Ghahramani Lecture 10 and 11: Text and Discrete Distributions 5 / 24. ... Rasmussen and Ghahramani Lecture 10 and 11: Text and Discrete Distributions 24 / 24. -
- Machine Learning 4F13, Spring 2014
https://mlg.eng.cam.ac.uk/teaching/4f13/1314/lect1011.pdf19 Nov 2023: Rasmussen and Ghahramani Lecture 10 and 11: Text and Discrete Distributions 5 / 24. ... Rasmussen and Ghahramani Lecture 10 and 11: Text and Discrete Distributions 24 / 24. -
Gaussian Processes — a brief introduction
https://mlg.eng.cam.ac.uk/teaching/4f13/2324/gp.pdf19 Nov 2023: 64. 20. 24. 6. 6. 4. 2. 0. 2. 4. 6. ... Rasmussen Gaussian Processes October 23th, 2023 24 / 27. 43. 21. -
Now You See Me (CME): Concept-based Model Extraction
https://mlg.eng.cam.ac.uk/adrian/AIMLAI20-CME.pdf16 Jul 2024: Model ExtractionModel extraction techniques use rules [20, 21, 22], de-cision trees [23, 24], or other more readily explainablemodels [25] to approximate complex models, in orderto study their behaviour. ... 24] M. Sato, H. Tsukimoto, Rule extraction -
3F3: Signal and Pattern Processing Lecture 5: Dimensionality…
https://mlg.eng.cam.ac.uk/teaching/3f3/1011/lect5.pdf19 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 -
An introduction to Flow Matching · Cambridge MLG Blog
https://mlg.eng.cam.ac.uk/blog/2024/01/20/flow-matching.html12 Apr 2024: Figure 24: One-sided conditioning (Lipman et al., 2022). Figure 25: Two-sided conditioning (Tong et al., 2023). -
Ode to an ODE Krzysztof Choromanski ∗Robotics at Google ...
https://mlg.eng.cam.ac.uk/adrian/NeurIPS20-ODEtoODE.pdf16 Jul 2024: MIT Press, 2016. [24] Aditya Grover, Eric Wang, Aaron Zweig, and Stefano Ermon. ... In Proceedings of the 33nd International Conference on Machine Learning,ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 2034–2042, 2016. -
Gaussian Process
https://mlg.eng.cam.ac.uk/teaching/4f13/2122/gaussian%20process.pdf19 Nov 2023: 64. 20. 24. 6. 6. 4. 2. 0. 2. 4. 6. -
Gaussian Process
https://mlg.eng.cam.ac.uk/teaching/4f13/1617/gaussian%20process.pdf19 Nov 2023: 64. 20. 24. 6. 6. 4. 2. 0. 2. 4. 6. -
- 4F13: Machine Learning
https://mlg.eng.cam.ac.uk/teaching/4f13/0809/lect01.pdf19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 1: Introduction to Machine Learning January 16th, 2009 24 / 26. -
paper.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/RotVanMooetal10.pdf13 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. -
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. -
Gaussian Process
https://mlg.eng.cam.ac.uk/teaching/4f13/2324/gaussian%20process.pdf19 Nov 2023: 64. 20. 24. 6. 6. 4. 2. 0. 2. 4. 6. -
t.dvi
https://mlg.eng.cam.ac.uk/zoubin/papers/MinGha03.pdf27 Jan 2023: HG e! lk! (24). wherek ] a? b i $W BA (25)k! ]! -
C:/Users/Adrian/Documents/GitHub/betheClean/docs/nb-UAI.dvi
https://mlg.eng.cam.ac.uk/adrian/nb-UAI.pdf16 Jul 2024: Approximating the Bethe Partition Function. Adrian WellerDepartment of Computer Science. Columbia UniversityNew York NY 10027. adrian@cs.columbia.edu. Tony JebaraDepartment of Computer Science. Columbia UniversityNew York NY 10027. jebara@cs.columbia -
Scalingin a Hierar chical Unsupervised Network 1 Zoubin Ghahramani,2…
https://mlg.eng.cam.ac.uk/pub/pdf/GhaKorHin99a.pdf13 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). -
The Geometry of Random Features Krzysztof Choromanski∗1 Mark…
https://mlg.eng.cam.ac.uk/adrian/geometry.pdf16 Jul 2024: The Geometry of Random Features. Krzysztof Choromanski1 Mark Rowland2 Tamas Sarlos1 Vikas Sindhwani1 Richard E. Turner2 Adrian Weller231Google Brain, NY 2University of Cambridge, UK 3The Alan Turing Institute, UK. Abstract. We present an in-depth -
- 4F13: Machine Learning
https://mlg.eng.cam.ac.uk/teaching/4f13/0708/lect01.pdf19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 1: Introduction to Machine Learning January 18th, 2008 24 / 26. -
Gauged Mini-Bucket Elimination for Approximate Inference Sungsoo Ahn…
https://mlg.eng.cam.ac.uk/adrian/Gauge_for_Holder_Inference.pdf16 Jul 2024: We call these methods collectively Z-invariant methods. See [23, 24, 25] for discussions ofthe differences and relations between these methods. ... 24] G. David Forney Jr and Pascal O. Vontobel. Par-tition functions of normal factor graphs. -
- 4F13: Machine Learning
https://mlg.eng.cam.ac.uk/teaching/4f13/0708/lect07.pdf19 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. -
- 4F13: Machine Learning
https://mlg.eng.cam.ac.uk/teaching/4f13/0708/lect04.pdf19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 4: Graphical Models January 30th, 2008 24 / 1. -
What Keeps a Bayesian Awake At Night? Part 2: Night Time · Cambridge…
https://mlg.eng.cam.ac.uk/blog/2021/03/31/what-keeps-a-bayesian-awake-at-night-part-2.html12 Apr 2024: Paris (1994, p. 24): “[W]hen an attempt is made to fill in all the details some of the attractiveness of the original is lost.”. -
- 4F13: Machine Learning
https://mlg.eng.cam.ac.uk/teaching/4f13/0809/lect04.pdf19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 4: Graphical Models January 27th, 2009 24 / 25. -
- 4F13: Machine Learning
https://mlg.eng.cam.ac.uk/teaching/4f13/0910/lect04.pdf19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 4: Graphical Models January 27th, 2010 24 / 25. -
- 4F13: Machine Learning
https://mlg.eng.cam.ac.uk/teaching/4f13/0910/lect07.pdf19 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. -
- 4F13: Machine Learning
https://mlg.eng.cam.ac.uk/teaching/4f13/0809/lect07.pdf19 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. -
PILCO: A Model-Based and Data-Efficient Approach to Policy Search
https://mlg.eng.cam.ac.uk/pub/pdf/DeiRas11.pdf13 Feb 2023: Ext [c(xt)] =. c(xt)N. (xt |µt, Σt. )dxt , (24). t = 0,. ... 10)–(12), (24).7: Gradient-based policy improvement, see. Sec. 2.3: get dJπ(θ)/ dθ, Eqs. -
chuesann.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/ChuGhaWil04b.pdf13 Feb 2023: H70.13% 70.77% 72.28% 71.70%. QobsE. 46.51% 24.69% 46.86% 23.96%Qobs. C73.29% 70.52% 72.64% 70.86%. -
- 4F13: Machine Learning
https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect04.pdf19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 4: Graphical Models 24 / 26. -
Marc P. Deisenroth, Carl E. Rasmussen, and Jan Peters: ...
https://mlg.eng.cam.ac.uk/pub/pdf/DeiRasPet08.pdf13 Feb 2023: pages 19–24, Bruges, Belgium, April 2008. Model-Based Reinforcement Learning withContinuous States and Actions. -
nips.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/Ras96.pdf13 Feb 2023: 0.2. 0.3. 0.4. 0.5. 0.6. House. 24 48 96 1920. 0.05.
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