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31 - 40 of 51 search results for KaKaoTalk:ZA31 24 24 |u:mlg.eng.cam.ac.uk where 0 match all words and 51 match some words.

  1. Results that match 2 of 3 words

  2. An Introduction to LP Relaxations for MAP Inference

    https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW2-LP.pdf
    16 May 2024: x3. x4. x5. x6. s. balanced almost balanced(attractive up to flipping) 24 / 41.

  3. Gauged Mini-Bucket Elimination for Approximate Inference Sungsoo Ahn…

    https://mlg.eng.cam.ac.uk/adrian/Gauge_for_Holder_Inference.pdf
    16 May 2024: We call these methods collectively Z-invariant methods. See [23, 24, 25] for discussions ofthe differences and relations between these methods. ... 24] G. David Forney Jr and Pascal O. Vontobel. Par-tition functions of normal factor graphs.

  4. Conditions Beyond Treewidth for Tightness of Higher-order LP…

    https://mlg.eng.cam.ac.uk/adrian/conditions.pdf
    16 May 2024: Conditions Beyond Treewidth for Tightness of Higher-order LP Relaxations. Mark Rowland Aldo Pacchiano Adrian WellerUniversity of Cambridge UC Berkeley University of Cambridge. Abstract. Linear programming (LP) relaxations are a pop-ular method to

  5. Leader Stochastic Gradient Descent for DistributedTraining of Deep…

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS2019_LSGD_preprint.pdf
    16 May 2024: Landscape symmetries are commonin a plethora of non-convex problems [18, 19, 20, 21, 22], including deep learning [23, 24, 25, 26]. ... Understanding symmetries in deep networks. CoRR,abs/1511.01029, 2015. [24] A. Choromanska, M.

  6. One-network Adversarial Fairness

    https://mlg.eng.cam.ac.uk/adrian/AAAI2019_OneNetworkAdversarialFairness.pdf
    16 May 2024: log(1/δ). n(24). The term[4nI(x D0). 4nI(x D1). ]is what is estimated. ... From the latter note and (24), the two classifiers of theadversarial formulation proposed in (9) in the main docu-ment can be interpreted w.r.t.

  7. Unifying Orthogonal Monte Carlo Methods

    https://mlg.eng.cam.ac.uk/adrian/ICML2019-unified.pdf
    16 May 2024: E[x̃λỹλx̃λvỹλv x̃. 2λỹ. 2λv. ] (24)We next show the following. Lemma A.7.

  8. Clamping Variables and Approximate Inference Adrian WellerColumbia…

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS14-clamp.pdf
    16 May 2024: Journal of Automated Reasoning, 24(1-2):225–275, 2000. N. Ruozzi. The Bethe partition function of log-supermodular graphical models.

  9. Uprooting and Rerooting Higher-Order GraphicalModels Mark…

    https://mlg.eng.cam.ac.uk/adrian/uprooting-higher-order.pdf
    16 May 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. Geometrically Coupled Monte Carlo Sampling Mark Rowland∗University of …

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS18-gcmc.pdf
    16 May 2024: Geometrically Coupled Monte Carlo Sampling. Mark RowlandUniversity of Cambridgemr504@cam.ac.uk. Krzysztof ChoromanskiGoogle Brain Roboticskchoro@google.com. François ChalusUniversity of Cambridgechalusf3@gmail.com. Aldo PacchianoUniversity of

  11. Structured Evolution with Compact Architectures for Scalable Policy…

    https://mlg.eng.cam.ac.uk/adrian/structured_icml_full.pdf
    16 May 2024: Structured Evolution with Compact Architecturesfor Scalable Policy Optimization. Krzysztof Choromanski 1 Mark Rowland 2 Vikas Sindhwani 1 Richard E. Turner 2 Adrian Weller 2 3. AbstractWe present a new method of blackbox optimiza-tion via gradient

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