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  1. Results that match 1 of 2 words

  2. paperftp.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/modul.pdf
    27 Jan 2023: As in previous studies of the visuomo-tor system [23, 24, 25], the internal structure of thesystem can be probed by investigating the generaliza-tion properties in response to novel inputs, ... Constraints on learning new mappingsbetween perceptual
  3. Dirichlet Process Mixture Models for Verb Clustering Andreas Vlachos…

    https://mlg.eng.cam.ac.uk/pub/pdf/VlaGhaKor08.pdf
    13 Feb 2023: gauss 78.54% 50.22% 61.26%34 classes. vanilla 70.24% 78.94% 74.34%link34 100 73.19% 79.24& 76.10%.
  4. SMEM Algorithm for Mixture Models

    https://mlg.eng.cam.ac.uk/pub/pdf/UedNakGha98a.pdf
    13 Feb 2023: initiall value EM DAEM. mean -159.1 -148.2 -147.9 Training std 1.n 0.24 0.04 data.
  5. 13 Feb 2023: 24, pp. 195–220, 2005. [14] T. Smith and R. Simmons, “Heuristic search value iteration for POMDPs,” inProc. ... 24] J. H. Robert, R. St-aubin, A. Hu, and C. Boutilier, “SPUDD: Stochastic planning using decisiondiagrams,” inUAI, pp.
  6. PROPAGATION OF UNCERTAINTY IN BAYESIAN KERNEL MODELS— APPLICATION TO…

    https://mlg.eng.cam.ac.uk/pub/pdf/QuiGirLarRas03.pdf
    13 Feb 2023: Lij = ki(u)kj (u) |2Λ1S I|12 (24). exp[2(u xd)>Λ1(2Λ1 S1)1Λ1(u xd). ],.
  7. Time-Sensitive Dirichlet Process Mixture Models Xiaojin Zhu Zoubin…

    https://mlg.eng.cam.ac.uk/zoubin/papers/tdpmTR.pdf
    27 Jan 2023: w(t, c) =. i:ti<t,si=c. k(t ti) =. eλ(tti) (24). λw(t, c) =. i:ti<t,si=c. (t ti)eλ(tti) (25). We then take a
  8. book

    https://mlg.eng.cam.ac.uk/pub/pdf/PerGhaPon07.pdf
    13 Feb 2023: 1.23). subject to:. wt φt(xn, ynt) wt φt(xn, yt) Mynt,yt ξnt n, t, yt (1.24). ... We end up having a significant reduction in the number ofconstraints2 in our optimisation formulation for CGMs in (1.23)-(1.24).
  9. PILCO: A Model-Based and Data-Efficient Approach to Policy Search

    https://mlg.eng.cam.ac.uk/pub/pdf/DeiRas11.pdf
    13 Feb 2023: Ext [c(xt)] =. c(xt)N. (xt |µt, Σt. )dxt , (24). t = 0,. ... 10)–(12), (24).7: Gradient-based policy improvement, see. Sec. 2.3: get dJπ(θ)/ dθ, Eqs.
  10. zglactive.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/zglactive.pdf
    27 Jan 2023: Combining Active Learning and Semi-Supervised LearningUsing Gaussian Fields and Harmonic Functions. Xiaojin Zhu. ZHUXJ@CS.CMU.EDUJohn Lafferty. LAFFERTY@CS.CMU.EDU. Zoubin Ghahramani. ZOUBIN@GATSBY.UCL.AC.UKSchool of Computer Science, Carnegie
  11. The Infinite Hidden Markov Model Matthew J. Beal Zoubin ...

    https://mlg.eng.cam.ac.uk/zoubin/papers/ihmm.pdf
    27 Jan 2023: The Infinite Hidden Markov Model. Matthew J. Beal Zoubin Ghahramani Carl Edward Rasmussen. Gatsby Computational Neuroscience UnitUniversity College London. 17 Queen Square, London WC1N 3AR, Englandhttp://www.gatsby.ucl.ac.uk. {m.beal,zoubin,edward

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