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Methods for Inference in Graphical Models
https://mlg.eng.cam.ac.uk/adrian/phd_FINAL.pdf16 May 2024: 91. 7.6.2 Test sets. 93. 7.7 Conclusions. 95. 8 Clamping Variables and Approximate Inference 96. ... cave functions. They have attracted attention in combinatorics (Lovász, 1983), economics (Topkis,. -
Bayesian nonparametrics andthe probabilistic approach to modelling…
https://mlg.eng.cam.ac.uk/pub/pdf/Gha12.pdf13 Feb 2023: Moreover, at test time, the DPM always allows for the possibility thata new test point (e.g. -
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https://mlg.eng.cam.ac.uk/pub/pdf/GhaHin00a.pdf13 Feb 2023: LETTER Communicated by Volker Tresp. Variational Learning for Switching State-Space Models. Zoubin GhahramaniGeoffrey E. HintonGatsby Computational Neuroscience Unit, University College London, London WC1N3AR, U.K. We introduce a new statistical -
Generalised Bayesian Matrix Factorisation Models Shakir Mohamed St…
https://mlg.eng.cam.ac.uk/pub/pdf/Moh11.pdf13 Feb 2023: a) RMSE on training data (b) RMSE on test data(c) NLP (shown on a log-scale to aid viewing). ... 1.1 The Ubiquitous Latent Variable. Models with latent variables hold a central role in in the analysis of data in a diverseset of research areas spanning -
Bayesian Learning forData-Efficient Control Rowan McAllister…
https://mlg.eng.cam.ac.uk/pub/pdf/Mca16.pdf13 Feb 2023: We test our method on the cartpole swing-up task, which involvesnonlinear dynamics and requires nonlinear control. ... Learning control of dynamical systems is a broad subject. Applications rangeform industrial (refining, manufacturing, power),
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