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1 - 10 of 38 search results for katalk:PC53 24 / |u:mlg.eng.cam.ac.uk where 0 match all words and 38 match some words.

  1. Results that match 1 of 2 words

  2. Understanding the Bethe Approximation: When and How can it go Wrong?

    https://mlg.eng.cam.ac.uk/adrian/pabc.pdf
    21 Mar 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.

  3. Blind Justice: Fairness with Encrypted Sensitive Attributes

    https://mlg.eng.cam.ac.uk/adrian/poster_ICML18_BlindJustice.pdf
    21 Mar 2024: Bank. Datasets and Feasibility. Adult Bank COMPAS German SQFn training examples 214 215 212 29 216d features 51 62 7 24 23p sensitive attributes 1 1 7 1 1certification 802 ms

  4. Clamping Variables and Approximate Inference

    https://mlg.eng.cam.ac.uk/adrian/newsclamp.pdf
    21 Mar 2024: 0.1. 0.2. 0.3. 0.4. max interaction strength W 24 / 19.

  5. Revisiting the Limits of MAP Inference by MWSS on Perfect Graphs

    https://mlg.eng.cam.ac.uk/adrian/slides-revisit.pdf
    21 Mar 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). •

  6. Clamping Variables and Approximate Inference

    https://mlg.eng.cam.ac.uk/adrian/slides_msr2.pdf
    21 Mar 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.

  7. Directed and Undirected Graphical Models

    https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW1-models.pdf
    21 Mar 2024: 23 / 26. Directed and undirected models are different. 24 / 26. ... Directed and undirected models are different. With <3 edges,. 24 / 26.

  8. Junction Tree, BP and Variational Methods

    https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW3-approx.pdf
    21 Mar 2024: 24 / 32. The log-partition function log Z. Since D(q‖p) 0, we have. ... a finite convex hull 3. M(G) = conv {φ(e), e X m} (5.24).

  9. The Case for Process Fairness in Learning:Feature Selection for ...

    https://mlg.eng.cam.ac.uk/adrian/grgic.pdf
    21 Mar 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

  10. Bounding the Integrality Distance ofLP Relaxations for Structured…

    https://mlg.eng.cam.ac.uk/adrian/OPT2016_paper_3.pdf
    21 Mar 2024: 24)Using the same approach to upper-bound Equation 22, we assume, without loss of generality, that(δ(µI ) θ) µ̃L (δ(µI ) θ. ′) ... m. (25). Using Equation 25 to upper-bound 22, and Equation 24 to upper-bound 23, we have that.

  11. Structured Prediction Models for Chord Transcription of Music Audio…

    https://mlg.eng.cam.ac.uk/adrian/icmla09adrian.pdf
    21 Mar 2024: To its right, 0-1 adds the features of the previous frame, so in this model each frame is represented by 24 dimensions.

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