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A New Machine Learning Library

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If you have a question about this talk, please contact Emli-Mari Nel.

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This will be an informal talk about a new machine learning library written (from scratch) in Java. I will talk about some of its nice properties, e.g. how it makes use of Java generics. I will show some nice example code, e.g. [1] fully generic sampling algorithms (Metropolis, etc.) which can be instantiated in 1 line of code for any space and distribution; [2] several elegant ways of constructing fully generic Pitman-Yor and Dirichlet-Processes, such that samples can themselves be used as distributions; [3] interesting dualities between stick-breaking constructions and distributions over natural numbers; [4] how to define distributions over arbitrary grammars in very few lines of code.

This talk is part of the Inference Group series.

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