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  2. ibpnips4.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/ibp-nips05.pdf
    27 Jan 2023: 5. 10. K+. 0 100 200 300 400 500 600 700 800 900 10000. ... 2. 4. α. 0 100 200 300 400 500 600 700 800 900 10000.
  3. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect1011.pdf
    19 Nov 2023: 2 1.5 1 0.5 0 0.5 1 1.5 200.10.2. 0.3. 0.4.
  4. Graph-based Semi-supervised Learning Zoubin Ghahramani Department of…

    https://mlg.eng.cam.ac.uk/zoubin/talks/lect3ssl.pdf
    27 Jan 2023: 2” (|LU| = 2200). 5 10 15 200.5. 0.6. 0.7. 0.8. ... 200-209.• Lawrence, N. D., & Jordan, M. I. (2005). Semi-supervised learning via Gaussian processes.
  5. Nested sampling for Potts models Iain MurrayGatsby Computational…

    https://mlg.eng.cam.ac.uk/zoubin/papers/nips05nested.pdf
    27 Jan 2023: This. 1e-120. 1e-100. 1e-80. 1e-60. 1e-40. 1e-20. 1. 0 200 400 600 800 1000 1200 1400 1600 1800 2000. ... 100. 200. 300. (b). 5 0 50. 50. 100. 150. 200.
  6. Message Passing Algorithms for Dirichlet Diffusion Trees

    https://mlg.eng.cam.ac.uk/pub/pdf/KnoGaeGha11.pdf
    13 Feb 2023: Macaque skull measurements (N = 200,D = 10).We the macaque skull measurement data of Adamset al. ... We calculate predictive log like-lihoods on four splits into 1800 training and 200 testgenes.
  7. A New Approach to Data Driven Clustering Arik Azran ...

    https://mlg.eng.cam.ac.uk/zoubin/papers/AzrGhaICML06.pdf
    27 Jan 2023: 250. 300. 350. P340 ; K=10. 50 100 150 200 250 300 350. ... 50. 100. 150. 200. 250. 300. 350. P4.8e11 ; K=4. Figure 1.
  8. Factored Contextual Policy Search with Bayesian Optimization Robert…

    https://mlg.eng.cam.ac.uk/pub/pdf/PinKarKupetal19.pdf
    13 Feb 2023: If. 0 100 200 300 400 500episode. 0.20. 0.15. 0.10. 0.05. ... We compare FACESto ACES, and use 200 representer points to approximate theacquisition functions.
  9. Infinite Sparse Factor Analysis and InfiniteIndependent Components…

    https://mlg.eng.cam.ac.uk/zoubin/papers/ica07knowles.pdf
    27 Jan 2023: generated data with D = 7, K =6, N = 200, the Z matrix shown in Figure 1(a), and Gaussian or Laplaciansource distributions.
  10. chu05a.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/ChuGha05a.pdf
    13 Feb 2023: Training data size. with Mean Imputation. 50 100 150 200 250 3000.6. ... 1032. GAUSSIAN PROCESSES FORORDINAL REGRESSION. 1 3 5 10 40 200 1000 126000.
  11. On the Convergence of Bound Optimization Algorithms Ruslan…

    https://mlg.eng.cam.ac.uk/pub/pdf/SalRowGha03a.pdf
    13 Feb 2023: 0 100 200 300 400 500 600. 0.06. 0.04. 0.02. 0. ... EM: Hidden Markov Models. 0 100 200 300 400 500 600 700 800 900 10004.

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