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91 - 100 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. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0910/lect01.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 1: Introduction to Machine Learning January 14th, 2010 24 / 26.

  3. The Geometry of Random Features Krzysztof Choromanski∗1 Mark…

    https://mlg.eng.cam.ac.uk/adrian/geometry.pdf
    19 Jun 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

  4. - Machine Learning 4F13, Spring 2014

    https://mlg.eng.cam.ac.uk/teaching/4f13/1314/lect0102.pdf
    19 Nov 2023: 4. 3. 2. 1. 0. 1. 2. 3. Rasmussen and Ghahramani Lecture 1 and 2: Probabilistic Regression 24 / 36.

  5. FAST ONLINE ANOMALY DETECTION USING SCAN STATISTICS Ryan Turner ...

    https://mlg.eng.cam.ac.uk/pub/pdf/TurBotGha10.pdf
    13 Feb 2023: λ̂(t t) =Ni=1. ueu(t+tti) = eutNi=1. ueu(tti). = eutλ̂(t). (24). If a new event has occurred at t we must add k(0) at theend: ... Note that as t these equations approachthose without edge correction, (24), as expected.

  6. zglactive.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/zglactive.pdf
    27 Jan 2023: Combining Active Learning and Semi-Supervised LearningUsing Gaussian Fields and Harmonic Functions. Xiaojin Zhu. ZHUXJ@CS.CMU.EDUJohn Lafferty. LAFFERTY@CS.CMU.EDU. Zoubin Ghahramani. ZOUBIN@GATSBY.UCL.AC.UKSchool of Computer Science, Carnegie

  7. - Machine Learning 4F13, Spring 2015

    https://mlg.eng.cam.ac.uk/teaching/4f13/1415/lect0102.pdf
    19 Nov 2023: 1. 0. 1. 2. 3. Samples from the posteriorRasmussen and Ghahramani Lecture 1 and 2: Probabilistic Regression 24 / 37.

  8. - Machine Learning 4F13, Michaelmas 2015

    https://mlg.eng.cam.ac.uk/teaching/4f13/1516/lect0102.pdf
    19 Nov 2023: 1. 0. 1. 2. 3. Samples from the posteriorGhahramani Lecture 1 and 2: Probabilistic Regression 24 / 38.

  9. Uprooting and Rerooting Higher-Order GraphicalModels Mark…

    https://mlg.eng.cam.ac.uk/adrian/uprooting-higher-order.pdf
    19 Jun 2024: 4], which relates to generalized belief propagation,24) and MAP inference (using loopy belief propagation, LBP [9]). ... InArtificial Intelligence and Statistics (AISTATS), 2016. [24] J. Yedidia, W. Freeman, and Y.

  10. Working Draft 1 Accountability of AI Under the Law: ...

    https://mlg.eng.cam.ac.uk/adrian/SSRN-id3064761-Dec19.pdf
    19 Jun 2024: 24. Furthermore, an explanation must also provide the correct type of information in order for it to be useful. ... 24 Wachter, Right to Explanation, supra note 18. For a discussion about legibility of algorithmic systems more broadly, see Gianclaudio

  11. Bayesian Classifier Combination Hyun-Chul Kim Zoubin GhahramaniKorea…

    https://mlg.eng.cam.ac.uk/pub/pdf/KimGha12.pdf
    13 Feb 2023: Bagging predictors. Ma-chine Learning 24, 123–140. Breiman, L., October 2001. Random forests. ... Vol. 24. pp. 791–798. Takács, G., Pilászy, I., Németh, B., Tikk, D., 2007.

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