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  2. - Machine Learning 4F13, Spring 2015

    https://mlg.eng.cam.ac.uk/teaching/4f13/1415/lect1011.pdf
    19 Nov 2023: Rasmussen and Ghahramani Lecture 10 and 11: Text and Discrete Distributions 5 / 24. ... Rasmussen and Ghahramani Lecture 10 and 11: Text and Discrete Distributions 24 / 24.
  3. - Machine Learning 4F13, Spring 2014

    https://mlg.eng.cam.ac.uk/teaching/4f13/1314/lect1011.pdf
    19 Nov 2023: Rasmussen and Ghahramani Lecture 10 and 11: Text and Discrete Distributions 5 / 24. ... Rasmussen and Ghahramani Lecture 10 and 11: Text and Discrete Distributions 24 / 24.
  4. Engineering Tripos Part IB SECOND YEAR PART IB Paper ...

    https://mlg.eng.cam.ac.uk/teaching/1BP7/1819/IBP7ex76.pdf
    19 Nov 2023: 4.40 5.00 13.24 4.84 3.31 11.54 7.42 9.39 3.75 1.39 13.89 31.38 17.48 11.91 2.26
  5. Gaussian Processes — a brief introduction

    https://mlg.eng.cam.ac.uk/teaching/4f13/2324/gp.pdf
    19 Nov 2023: 64. 20. 24. 6. 6. 4. 2. 0. 2. 4. 6. ... Rasmussen Gaussian Processes October 23th, 2023 24 / 27. 43. 21.
  6. An introduction to Flow Matching · Cambridge MLG Blog

    https://mlg.eng.cam.ac.uk/blog/2024/01/20/flow-matching.html
    12 Apr 2024: Figure 24: One-sided conditioning (Lipman et al., 2022). Figure 25: Two-sided conditioning (Tong et al., 2023).
  7. 3F3: Signal and Pattern Processing Lecture 5: Dimensionality…

    https://mlg.eng.cam.ac.uk/teaching/3f3/1011/lect5.pdf
    19 Nov 2023: Dataset Data dim. Sample size MLE Regression Corr. dim.Swiss roll 3 1000 2.1(0.02) 1.8(0.03) 2.0(0.24)Faces 64 64 698 4.3
  8. Conditions Beyond Treewidth for Tightness of Higher-order LP…

    https://mlg.eng.cam.ac.uk/adrian/conditions.pdf
    16 Jul 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
  9. Unifying Orthogonal Monte Carlo Methods

    https://mlg.eng.cam.ac.uk/adrian/ICML2019-unified.pdf
    16 Jul 2024: E[x̃λỹλx̃λvỹλv x̃. 2λỹ. 2λv. ] (24)We next show the following. Lemma A.7.
  10. Clamping Variables and Approximate Inference Adrian WellerColumbia…

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

    https://mlg.eng.cam.ac.uk/adrian/AAAI2019_OneNetworkAdversarialFairness.pdf
    16 Jul 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.
  12. Structured Evolution with Compact Architectures for Scalable Policy…

    https://mlg.eng.cam.ac.uk/adrian/structured_icml_full.pdf
    16 Jul 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
  13. Leader Stochastic Gradient Descent for DistributedTraining of Deep…

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS2019_LSGD_preprint.pdf
    16 Jul 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.
  14. Gaussian Process

    https://mlg.eng.cam.ac.uk/teaching/4f13/2122/gaussian%20process.pdf
    19 Nov 2023: 64. 20. 24. 6. 6. 4. 2. 0. 2. 4. 6.
  15. Gaussian Process

    https://mlg.eng.cam.ac.uk/teaching/4f13/1819/gaussian%20process.pdf
    19 Nov 2023: 64. 20. 24. 6. 6. 4. 2. 0. 2. 4. 6.
  16. Scalingin a Hierar chical Unsupervised Network 1 Zoubin Ghahramani,2…

    https://mlg.eng.cam.ac.uk/zoubin/papers/scaling.pdf
    27 Jan 2023: Eachof the 24 hiddenunits in the middle hiddenlayerwas connectedto 9 consecutive visible units from eacheye,i.e. ... e), 1-24-36(b,f),1-48-72(c,g), 1-72-108(d,h).
  17. Uprooting and Rerooting Higher-Order GraphicalModels Mark…

    https://mlg.eng.cam.ac.uk/adrian/uprooting-higher-order.pdf
    16 Jul 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.
  18. Working Draft 1 Accountability of AI Under the Law: ...

    https://mlg.eng.cam.ac.uk/adrian/SSRN-id3064761-Dec19.pdf
    16 Jul 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
  19. linsys-new.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/tr-96-2.pdf
    27 Jan 2023: Initial state covariance:@Q@V 11 = 12V1 12(P1 x̂101 1x̂01 101) (23)V new1 = P1 x̂1x̂01 (24)The above equations can be readily generalized to multiple observation sequences, withone subtlety
  20. 3F3: Signal and Pattern Processing Lecture 1: Introduction to ...

    https://mlg.eng.cam.ac.uk/teaching/3f3/1011/lect1.pdf
    19 Nov 2023: Dataset Data dim. Sample size MLE Regression Corr. dim.Swiss roll 3 1000 2.1(0.02) 1.8(0.03) 2.0(0.24)Faces 64 64 698 4.3
  21. ICML-Presentation

    https://mlg.eng.cam.ac.uk/zoubin/talks/ICML-Presentation.pdf
    27 Jan 2023: International Conference onMachine Learning. Corvallis, Oregon, June 20-24 2007. Summary Presentation:Statistics, Awards, Comments.

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