Coconut: Optimizing computations for machine learning

Add to your list(s) Download to your calendar using vCal

• Andrew Fitzgibbon (Microsoft Research Cambridge)
• Tuesday 29 September 2009, 14:00-15:00
• Engineering Department, CBL Room 438.

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.

This talk is included in these lists:

• Seminar
• All Talks (aka the CURE list)
• Biology
• CBL important
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge Neuroscience Seminars
• Cambridge talks
• Chris Davis' list
• Creating transparent intact animal organs for high-resolution 3D deep-tissue imaging
• Engineering Department, CBL Room 438
• Featured lists
• Guy Emerson's list
• Hanchen DaDaDash
• Inference Group Summary
• Information Engineering Division seminar list
• Interested Talks
• Joint Machine Learning Seminars
• Life Science
• Life Sciences
• ML
• Machine Learning @ CUED
• Machine Learning Summary
• Neuroscience
• Neuroscience Seminars
• Neuroscience Seminars
• Required lists for MLG
• Simon Baker's List
• Stem Cells & Regenerative Medicine
• Trust & Technology Initiative - interesting events
• bld31
• dh539
• kt532's list
• ndk22's list
• ob366-ai4er
• rp587
• yk373's list

Note that ex-directory lists are not shown.