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ICML-Presentation
https://mlg.eng.cam.ac.uk/zoubin/talks/ICML-Presentation.pdf27 Jan 2023: International Conference onMachine Learning. Corvallis, Oregon, June 20-24 2007. Summary Presentation:Statistics, Awards, Comments. -
- 4F13: Machine Learning
https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect07.pdf19 Nov 2023: But we can not usually simulate Hamiltonian dynamics exactly. Ghahramani & Rasmussen (CUED) Lecture 7 and 8: Markov Chain Monte Carlo 24 / 28. -
- Machine Learning 4F13, Michaelmas 2015
https://mlg.eng.cam.ac.uk/teaching/4f13/1516/lect0304.pdf19 Nov 2023: 1 0 1 22. 1. 0. 1. 2M=0. 1 0 1 24. ... 1. 2M=0. 1 0 1 24. 2. 0. 2. 4. M=1. -
- Machine Learning 4F13, Spring 2014
https://mlg.eng.cam.ac.uk/teaching/4f13/1314/lect0304.pdf19 Nov 2023: 1. 0. 1. 2M=0. 1 0 1 24. 2. 0. 2. ... 1. 2M=0. 1 0 1 24. 2. 0. 2. 4. M=1. -
- Machine Learning 4F13, Spring 2015
https://mlg.eng.cam.ac.uk/teaching/4f13/1415/lect0304.pdf19 Nov 2023: 1. 0. 1. 2M=0. 1 0 1 24. 2. 0. 2. ... 1. 2M=0. 1 0 1 24. 2. 0. 2. 4. M=1. -
paper.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/KimGha08.pdf13 Feb 2023: GPC log-ev -24.40.6 -41.80.8 -51.81.1 -60.21.0error(%) 3.700.36 4.900.77 7.500.88 8.151.42. robust log-ev ... 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. -
WolGha05 handout
https://mlg.eng.cam.ac.uk/zoubin/papers/WolGha06.pdf27 Jan 2023: L. Generalization, similarity, and Bayesian inference. Behav Brain Sci 24,. 629-40; discussion 652-791 (2001). -
Inferring a measure of physiological age frommultiple ageing related…
https://mlg.eng.cam.ac.uk/pub/pdf/KnoParGlaWin11.pdf13 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. -
https://mlg.eng.cam.ac.uk/teaching/4f13/1112/cw/mauna.txt
https://mlg.eng.cam.ac.uk/teaching/4f13/1112/cw/mauna.txt19 Nov 2023: 875 316.69 1962.958 317.70 1963.042 318.74 1963.125 319.08 1963.208 319.86 1963.292 321.39 1963.375 322.24 1963.458 321.47 ... 24 1964.458 321.89 1964.542 320.44 1964.625 318.70 1964.708 316.70 1964.792 316.79 1964.875 317.79 1964.958 318.71 1965.042 -
book
https://mlg.eng.cam.ac.uk/pub/pdf/PerGhaPon07.pdf13 Feb 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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