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61 - 70 of 314 search results for katalk:PC53 24 / |u:mlg.eng.cam.ac.uk where 0 match all words and 314 match some words.

  1. Results that match 1 of 2 words

  2. From Parity to Preference-based Notionsof Fairness in Classification…

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS17-from-parity-to-preference.pdf
    19 Jun 2024: decisionoutcomes. A number of learning mechanisms have been proposed to achieve parity in treatment [24],. ... Angwin. https://github.com/propublica/compas-analysis, 2016. [24] B. T. Luong, S. Ruggieri, and F.

  3. doi:10.1016/j.patrec.2005.09.027

    https://mlg.eng.cam.ac.uk/pub/pdf/KimKimGha06a.pdf
    13 Feb 2023: Martinez, A., Benavente, R., 1998. The ar face database. CVC TechnicalReport #24. ... Pattern Anal. Mach. Intell. 24 (5), 707–711. Neal, R., 1997. Monte Carlo implementation of Gaussian process modelsfor Bayesian regression and classification.

  4. A Brief Overview of Nonparametric Bayesian Models NIPS 2009 ...

    https://mlg.eng.cam.ac.uk/zoubin/talks/nips09npb.pdf
    27 Jan 2023: 21). Given s, the distribution of Z becomes:. p( Z | x , s, µ ( 1 : ) ) p( Z | x , µ ( 1 : ) ) 1µ I (0 s µ ) (24).

  5. PowerPoint Presentation

    https://mlg.eng.cam.ac.uk/capp-workshop/slides/chris.pdf
    23 Nov 2022: Slide Number 21. Slide Number 22. Slide Number 23. Slide Number 24.

  6. Augmented Attribute Representations Viktoriia Sharmanska1, Novi…

    https://mlg.eng.cam.ac.uk/pub/pdf/ShaQuaLam12.pdf
    13 Feb 2023: There are 50 animals classesin this dataset. The dataset also contains semantic information in the form of an85-dimensional Osherson’s [24] attribute vector for each animal class. ... In: CVPR. (2010) 3027–3034. 24. Osherson, D.N., Stern, J., Wilkie,

  7. Methods for Inference in Graphical Models

    https://mlg.eng.cam.ac.uk/adrian/phd_FINAL.pdf
    19 Jun 2024: 3 Additional Background 24. 3.1 MAP Inference and Tractable Cases. 24.

  8. The Indian Buffet Process and Extensions Zoubin Ghahramani University …

    https://mlg.eng.cam.ac.uk/zoubin/talks/turin09.pdf
    27 Jan 2023: 21). Given s, the distribution of Z becomes:. p( Z | x , s, µ ( 1 : ) ) p( Z | x , µ ( 1 : ) ) 1µ I (0 s µ ) (24).

  9. Bayesian Hierarchical Clustering Katherine A. Heller…

    https://mlg.eng.cam.ac.uk/zoubin/papers/icml05heller.pdf
    27 Jan 2023: 1 2 3 4 5 6 8 9 10 7 11 12 13 14 15 16 18 20 19 17 21 22 23 24 25 26 270. ... 0.2231.24. 3.6. 59.9. 0 1 2 3 4 5 6 74.

  10. Encyclopedia of Cognitive Science—Author Stylesheet ©Copyright…

    https://mlg.eng.cam.ac.uk/zoubin/papers/ECS-infotheory02.pdf
    27 Jan 2023: After the neighbour tells you that he lives on the top floor, the probability of X drops to 0 for 24 of the 32 values and becomes 1/8 for the

  11. Tree-Structured Stick Breaking for Hierarchical Data Ryan Prescott…

    https://mlg.eng.cam.ac.uk/pub/pdf/AdaGhaJor10.pdf
    13 Feb 2023: To efficiently learn thenode parameters, we used Hamiltonian (hybrid) Monte Carlo (HMC) [24], taking 25 leapfrog HMCsteps, with a randomized step size. ... 24] Radford M. Neal. MCMC using Hamiltonian dynamics. In Handbook of Markov chain Monte

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