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Function draws from a dropout neural network. This new visualisation technique depicts the distribution over functions rather than the predictive distribution (see demo below). So I finally submitted my PhD thesis (given below). In it I organised the already published results on how to obtain uncertainty in deep learning, and collected lots of bits and pieces of new research I had lying around (wh
Practical Probabilistic Programming with Monads Adam Ścibior University of Cambridge, UK ams240@cam.ac.uk Zoubin Ghahramani University of Cambridge, UK zoubin@eng.cam.ac.uk Andrew D. Gordon Microsoft Research, UK and University of Edinburgh, UK adg@microsoft.com Abstract The machine learning community has recently shown a lot of inter- est in practical probabilistic programming systems that targe
I come from the Cambridge machine learning group. More than once I heard people referring to us as "the most Bayesian machine learning group in the world". I mean, we do work with probabilistic models and uncertainty on a daily basis. Maybe that's why it felt so weird playing with those deep learning models (I know, joining the party very late). You see, I spent the last several years working most
This page is a collection of references on nonparametric Bayesian methods and related topics. It was first written as bibliography for a three-part tutorial which I gave at the University of Cambridge and the Gatsby Unit at University College London, but now receives several thousand hits each month. Two talks I gave on the same topic at the Machine Learning Summer School 2009 are available on Vid
This 3-part tutorial addresses a machine learning audience, not assumed to be familiar with measure theory or the theory of stochastic processes. The course is intended to provide (1) an overview of what nonparametric Bayesian models exist beyond those already used in machine learning, and (2) a basic understanding of the mathematical construction of ''process'' models, both existing ones and new
A Tutorial on Dirichlet Processes and Hierarchical Dirichlet Processes Yee Whye Teh Gatsby Computational Neuroscience Unit University College London Mar 1, 2007 / CUED Yee Whye Teh (Gatsby) DP and HDP Tutorial Mar 1, 2007 / CUED 1 / 53 Outline 1 Dirichlet Processes Definitions, Existence, and Representations (recap) Applications Generalizations Generalizations 2 Hierarchical Dirichlet Processes Gr
The school takes place from 29 August - 10 September 2009 and will comprise ten days of both tutorial lectures and practicals. Courses will be held at the Centre for Mathematical Sciences (CMS) of the University of Cambridge, and at Microsoft Research Cambridge (MSRC).
organized by the University of Cambridge, Microsoft Research and Pascal University of Cambridge, 29 August - 10 September 2009 The 13th Machine Learning Summer School will be held in Cambridge, UK. This year's edition is organized by the University of Cambridge, Microsoft Research and PASCAL. The school will offer an overview of basic and advanced topics in machine learning through theoretical and
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