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

    https://mlg.eng.cam.ac.uk/adrian/conditions.pdf
    19 Jun 2024: Let P ={x Rm|Ax b} be a polytope for some A =[a1,. ... ak]. > Rkm, b Rk (for some k N). Thenfor v Ext(P), we have.
  3. SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases

    https://mlg.eng.cam.ac.uk/pub/pdf/LacPalDav13a.pdf
    13 Feb 2023: Despite its greedy nature, our experiments indicatethat SiGMa can efficiently match some of the world’s largestknowledge bases with high accuracy. ... Wethen obtained the entity name by scraping the correspond-ing IMDb page and matched it to our
  4. Bounding the Integrality Distance ofLP Relaxations for Structured…

    https://mlg.eng.cam.ac.uk/adrian/OPT2016_paper_3.pdf
    19 Jun 2024: Theorem 1. Let D denote a distribution over X. Let f : X Y Rd denote a feature mappingsuch that supx,y ‖f(x,y)‖2 B, for some finite constant, B <. ... Let f : X Y Rd denote afeature mapping such that supx,y ‖f(x,y)‖2 B, for some finite constant,
  5. Geometrically Coupled Monte Carlo Sampling Mark Rowland∗University of …

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS18-gcmc.pdf
    19 Jun 2024: Thus all 5 policies corre-spond to some variants of our GCMC mechanism.
  6. newroyftp.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/HinGha97a.pdf
    13 Feb 2023: of Gaussians is a model that describes some real data points in terms of underlyingGaussian clusters. ... Notice that the highpixel noise makes it dicult to infer the disparity in some images.
  7. newroyftp.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/RGBN.pdf
    27 Jan 2023: of Gaussians is a model that describes some real data points in terms of underlyingGaussian clusters. ... Notice that the highpixel noise makes it dicult to infer the disparity in some images.
  8. mfdt.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/AdaStoWil00a.pdf
    13 Feb 2023: We would expect those regions to correspondin some way to the objects that make up the picture. ... The affinity forbecoming a root, B? , was $. The model was sampled togenerate a suite of training data of some $$$ images fromwhich $$ were selected for
  9. paper.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/GhaGriSol06.pdf
    27 Jan 2023: We call this the exchangeable IBP (Griffiths andGhahramani, 2005). 3.5. Some properties of this distribution. ... Some of these images are shown in Figure 6 (b), togetherwith the feature vectors, zi, that were used to generate them.
  10. December 8, 2008 5:18 Connection Science connsci Connection…

    https://mlg.eng.cam.ac.uk/pub/pdf/DosRoy08.pdf
    13 Feb 2023: More formally, the beliefis a probability distribution over states. If the agent takes some action a and hearsobservation o from an initial belief b, it can easily update its belief using ... This situation will occur if the domainexpert estimates the
  11. To appear in Jordan, MI, Kearns MJ, and Solla, ...

    https://mlg.eng.cam.ac.uk/pub/pdf/GhaHin97a.pdf
    13 Feb 2023: Consider a unit, j, in some intermediate layer of a multilayer RGBN. ... Some images generated in this manner are shown in Fig. 3a.
  12. nips97ftp.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/nips97.pdf
    27 Jan 2023: a unit, j, in some intermediate layer of a multilayer RGBN. ... Some images generated in this manner are shown in Fig. 3a.a b Figure 3: a) Sample data from the stereodisparity problem.
  13. nipsderiv.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/SolMurLeietal03.pdf
    13 Feb 2023: As a second part of the experiment as shown in Figure 3(b), we now add some off-equilibrium function observations to the training set, by applying large control perturba-tions ... NIPS’97 Tutorial notes., 1999. [5] A. O’Hagan. Some Bayesian numerical
  14. Graphical Models Zoubin Ghahramani Department of…

    https://mlg.eng.cam.ac.uk/zoubin/talks/lect2gm.pdf
    27 Jan 2023: C. B. E. • Nodes correspond to random variables. • Edges represent statistical dependencies between the variables. ... p(A,B,C,D,E) = p(A)p(B)p(C|A,B)p(D|B,C)p(E|C,D). Inference: evaluate the probability distribution over some set of
  15. Background material crib-sheet Iain Murray , October 2003 Here ...

    https://mlg.eng.cam.ac.uk/zoubin/ml06/cribsheet.pdf
    27 Jan 2023: If anything here. is unclear you should to do some further reading and exercises. ... if Bx = y then x = B1y. Some other commonly used matrix definitions include:.
  16. ibp6.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/ibptr.pdf
    27 Jan 2023: N! =. N. j=1. 1. j= HN. (38). 4.6 Some properties of this distribution. ... These different views of the distribution specified by Equation 34 make it straightforwardto derive some of its properties.
  17. 19 Jun 2024: fairness. Our empirical analysissuggests that process fairness may be achieved with little cost to outcome fairness,but that some loss of accuracy is unavoidable. ... Consider a scenario for making some important decision. Let U denote the set of all
  18. coverage.eps

    https://mlg.eng.cam.ac.uk/pub/pdf/SilHelGhaetal10.pdf
    13 Feb 2023: k). corresponds to some pair Ai:Bj. However, this similarity is not directly de-rived from the similarity of the information contained in the distribution ofobjects themselves, {Ai} A, {Bi} B. ... Assume that A and B are two objectsclassified as linked
  19. Proc. Valencia / ISBA 8th World Meeting on Bayesian ...

    https://mlg.eng.cam.ac.uk/pub/pdf/GhaGriSol07.pdf
    13 Feb 2023: We call this the exchangeable IBP (Griffiths andGhahramani, 2005). 3.5. Some properties of this distribution. ... Some of these images are shown in Figure 6 (b), togetherwith the feature vectors, zi, that were used to generate them.
  20. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect01.pdf
    19 Nov 2023: The source coding problem. Imagine we have a set of symbols X = {a, b, c, d, e, f, g, h}.We want to transmit these symbols over some binary communication channel, ... Some distributions (cont). Uniform (x [a, b]):. p(x|a, b) ={ 1.
  21. iMGPE.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/RasGha02.pdf
    13 Feb 2023: 4. Optimize the hyper-hypers,a & b, for each of the variance parameters.5. ... Müller (eds.), pp. 554–560, MIT Press. Silverman, B. W. (1985). Some aspects of the spline smoothing approach to non-parametricregression curve fitting.J.

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