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11 - 20 of 312 search results for TALK:ZA31 24 / |u:mlg.eng.cam.ac.uk where 0 match all words and 312 match some words.
  1. Results that match 1 of 2 words

  2. Machine Learning 4f13 Lent 2011

    https://mlg.eng.cam.ac.uk/teaching/4f13/1011/
    19 Nov 2023: Feb 22, 24. Variational approximations (2L): KL divergences, mean field, expectation propagation.
  3. Machine Learning 4f13 Lent 2010

    https://mlg.eng.cam.ac.uk/teaching/4f13/0910/
    19 Nov 2023: Feb 24, 25. Model comparison (1L): Bayes factors, Occam's razor, BIC, Laplace approximations.
  4. Machine Learning 4f13 Lent 2014

    https://mlg.eng.cam.ac.uk/teaching/4f13/1314/
    19 Nov 2023: Feb 3, 6, 10, 13. Probabilistic Ranking. Feb 17, 20, 24, 27, Mar 3, 6.
  5. 27 Jan 2023: Week 14/15 (April 24, 29, May 1). Model Selection. Bayes Factors.
  6. 16 Jul 2024: 100. Maximum coupling strength y. BethelocalBethecycleBethemargTRWlocalTRWcycleTRWmarg. log partition error. 2 8 16 24 320. ... 2 8 16 24 320. 0.1. 0.2. 0.3. 0.4. Maximum coupling strength y.
  7. June

    https://mlg.eng.cam.ac.uk/carl/climate/1tonCO2.pdf
    14 Jul 2024: June 24, 2024 1 / 1.
  8. Clamping Variables and Approximate Inference

    https://mlg.eng.cam.ac.uk/adrian/slides_msr2.pdf
    16 Jul 2024: 24 / 21. Error in log Z vs number of clamps: complete graphs. ... 24 / 21. Time (secs) vs error in log Z for various methods.
  9. Revisiting the Limits of MAP Inference by MWSS on Perfect Graphs

    https://mlg.eng.cam.ac.uk/adrian/slides-revisit.pdf
    16 Jul 2024: Revisiting the Limits of MAP Inference by MWSSon Perfect Graphs. Adrian WellerUniversity of Cambridge. CP 2015Cork, Ireland. Slides and full paper athttp://mlg.eng.cam.ac.uk/adrian/. 1 / 21. Motivation: undirected graphical models (MRFs). •
  10. 13 Feb 2023: Publications, Machine Learning Group, Department of Engineering, Cambridge. current group:. [former members:. [by year:. [2022. James Urquhart Allingham, Florian Wenzel, Zelda E Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel,
  11. 16 Jul 2024: Age 46% 56% 32%Gender 24% 50% 22%Race 24% 35% 15%. Table 3: Comparing judgment of fairness of each feature, of 54 male users, when they have differentknowledge about the ... Age 42% 63% 33%Gender 24% 54% 23%Race 18% 43% 18%. Table 5: Comparing judgment

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