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PILCO: A Model-Based and Data-Efficient Approach to Policy Search
https://mlg.eng.cam.ac.uk/pub/pdf/DeiRas11.pdf13 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. -
Continuous Relaxations for Discrete Hamiltonian Monte Carlo
https://mlg.eng.cam.ac.uk/pub/pdf/ZhaSutSto12a.pdf13 Feb 2023: 1 Introduction. Discrete undirected graphical models have seen wide use in natural language processing [11, 24] andcomputer vision [19]. ... Weinberger, editors,Advances in Neural Information Processing Systems 24, pages 2744–2752. 2011. -
Cambridge Machine Learning Group Publications
https://mlg.eng.cam.ac.uk/pub/13 Feb 2023: Publications, Machine Learning Group, Department of Engineering, Cambridge. current group:. [former members:. [by year:. [2022. James Urquhart Allingham, Florian Wenzel, Zelda E Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, -
zglactive.dvi
https://mlg.eng.cam.ac.uk/zoubin/papers/zglactive.pdf27 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 -
PROPAGATION OF UNCERTAINTY IN BAYESIAN KERNEL MODELS— APPLICATION TO…
https://mlg.eng.cam.ac.uk/pub/pdf/QuiGirLarRas03.pdf13 Feb 2023: Lij = ki(u)kj (u) |2Λ1S I|12 (24). exp[2(u xd)>Λ1(2Λ1 S1)1Λ1(u xd). ],. -
Bayesian Knowledge Corroboration with LogicalRules and User Feedback…
https://mlg.eng.cam.ac.uk/pub/pdf/KasVanGraHer10.pdf13 Feb 2023: While [23, 24] represent the formalism of first-orderlogic by factor graph models, [27] and [29] deal with Bayesian networks appliedto first-order logic. ... 1094–1099. AAAI Press(2008). 24. Domingos, P., Richardson, M.: Markov Logic Networks. -
Time-Sensitive Dirichlet Process Mixture Models Xiaojin Zhu Zoubin…
https://mlg.eng.cam.ac.uk/zoubin/papers/tdpmTR.pdf27 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 -
1 Robust Filtering and Smoothing with Gaussian Processes Marc ...
https://mlg.eng.cam.ac.uk/pub/pdf/DeiTurHubetal12.pdf13 Feb 2023: Using the definition of S in (24), the productof the two Gaussians in (36) results in a new (unnormalized) Gaussianc14 N(xt1 |ψi, Ψ) with. ... 24] J. Kocijan, R. Murray-Smith, C. E. Rasmussen, and B. Likar, “PredictiveControl with Gaussian Process -
Factored Contextual Policy Search with Bayesian Optimization Robert…
https://mlg.eng.cam.ac.uk/pub/pdf/PinKarKupetal19.pdf13 Feb 2023: For the Gym tasks, weemploy an extension [24] of the DMP framework [12] toefficiently generate goal-directed trajectories. ... 16, no. 5, pp. 1190–1208, 1995. [24] J. Kober, K. Mülling, O. -
The Infinite Hidden Markov Model Matthew J. Beal Zoubin ...
https://mlg.eng.cam.ac.uk/pub/pdf/BeaGhaRas02.pdf13 Feb 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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