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Coconut: Optimizing computations for machine learning

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If you have a question about this talk, please contact Zoubin Ghahramani .

Matrix-vector notation is the predominant idiom in which machine learning formulae are expressed; some models, like Gaussian processes [5], would be extremely difficult to describe without it. Turning a matrix expression into a computer program is not always easy, however. Although good implementations of primitive matrix operations are available [2] as are packages like MATLAB [6], which provide a high-level interface to these primitives, two important tasks must still be carried out manually: (i) computing derivatives of matrix functions and (ii) turning a matrix expression into an efficient computer program. Not having tools to do this can and does harm research: even for the relatively simple example of fitting a linear regression model with gradient methods, the number of types and combinations of basis functions a researcher can experiment with is limited by the need to manually differentiate the objective function and write code for each version. We have addressed these issues by combining a symbolic matrix algebra engine with a superoptimizing compiler: an interesting learning problem in itself. We call our system Coconut.

This talk is part of the Machine Learning @ CUED series.

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