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21 - 30 of 52 search results for TALK:ZA31 24 / |u:mlg.eng.cam.ac.uk where 0 match all words and 52 match some words.

  1. Results that match 1 of 2 words

  2. What Keeps a Bayesian Awake At Night? Part 2: Night Time · Cambridge…

    https://mlg.eng.cam.ac.uk/blog/2021/03/31/what-keeps-a-bayesian-awake-at-night-part-2.html
    12 Apr 2024: Paris (1994, p. 24): “[W]hen an attempt is made to fill in all the details some of the attractiveness of the original is lost.”.

  3. Evaluating and Aggregating Feature-based Model Explanations

    https://mlg.eng.cam.ac.uk/adrian/IJCAI20_EvaluatingAndAggregating.pdf
    19 Jun 2024: µC(f, gSHAP) 1.94 0.26 1.36 0.36 2.33 0.23µC(f, gAVA) 1.93 0.24 1.24 0.32 2.61 0.29.

  4. Tightness of LP Relaxations for Almost Balanced Models Adrian ...

    https://mlg.eng.cam.ac.uk/adrian/tricam.pdf
    19 Jun 2024: Tightness of LP Relaxations for Almost Balanced Models. Adrian Weller Mark Rowland David SontagUniversity of Cambridge University of Cambridge New York University. Abstract. Linear programming (LP) relaxations are widelyused to attempt to identify a

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

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

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

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

  7. Unifying Orthogonal Monte Carlo Methods

    https://mlg.eng.cam.ac.uk/adrian/ICML2019-unified.pdf
    19 Jun 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
    19 Jun 2024: Journal of Automated Reasoning, 24(1-2):225–275, 2000. N. Ruozzi. The Bethe partition function of log-supermodular graphical models.

  9. One-network Adversarial Fairness

    https://mlg.eng.cam.ac.uk/adrian/AAAI2019_OneNetworkAdversarialFairness.pdf
    19 Jun 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.

  10. Structured Evolution with Compact Architectures for Scalable Policy…

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

  11. Leader Stochastic Gradient Descent for DistributedTraining of Deep…

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS2019_LSGD_preprint.pdf
    19 Jun 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.

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