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  1. Results that match 1 of 2 words

  2. Gaussian Process

    https://mlg.eng.cam.ac.uk/teaching/4f13/1819/gaussian%20process.pdf
    19 Nov 2023: 64. 20. 24. 6. 6. 4. 2. 0. 2. 4. 6.
  3. Scalingin a Hierar chical Unsupervised Network 1 Zoubin Ghahramani,2…

    https://mlg.eng.cam.ac.uk/zoubin/papers/scaling.pdf
    27 Jan 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).
  4. linsys-new.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/tr-96-2.pdf
    27 Jan 2023: Initial state covariance:@Q@V 11 = 12V1 12(P1 x̂101 1x̂01 101) (23)V new1 = P1 x̂1x̂01 (24)The above equations can be readily generalized to multiple observation sequences, withone subtlety
  5. 3F3: Signal and Pattern Processing Lecture 1: Introduction to ...

    https://mlg.eng.cam.ac.uk/teaching/3f3/1011/lect1.pdf
    19 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
  6. ML-IRL: Machine Learning in Real Life Workshop at ICLR ...

    https://mlg.eng.cam.ac.uk/adrian/ML_IRL_2020-CLUE.pdf
    19 Jun 2024: 6). LSATAl. (7). COMPASEp. (6). COMPASAl. (5) Total (24). Prolific Unc.
  7. ICML-Presentation

    https://mlg.eng.cam.ac.uk/zoubin/talks/ICML-Presentation.pdf
    27 Jan 2023: International Conference onMachine Learning. Corvallis, Oregon, June 20-24 2007. Summary Presentation:Statistics, Awards, Comments.
  8. Unsupervised Learning Lecture 6: Hierarchical and Nonlinear Models…

    https://mlg.eng.cam.ac.uk/zoubin/course03/lect6hier.pdf
    27 Jan 2023: Annual Review of. Neuroscience, 24. Applications of ICA and Related Methods. •
  9. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect01.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 1: Introduction to Machine Learning 24 / 26.
  10. Now You See Me (CME): Concept-based Model Extraction

    https://mlg.eng.cam.ac.uk/adrian/AIMLAI20-CME.pdf
    19 Jun 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
  11. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect07.pdf
    19 Nov 2023: But we can not usually simulate Hamiltonian dynamics exactly. Ghahramani & Rasmussen (CUED) Lecture 7 and 8: Markov Chain Monte Carlo 24 / 28.

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