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MLC@Home: Machine Learning Comprehension @ Home

MLC@Home is a distributed computing project dedicated to understanding and interpreting complex machine learning models, with an emphasis on neural networks. It uses the BOINC distributed computing platform.

Opening the Black Box
https://xkcd.com/1838/
XKCD #1838

Neural Networks have fuelled a machine learning revolution over the past decade that has led to machines accomplishing amazingly complex tasks. However, these models are largly black boxes: we know they work, but they are so complex (up to hundreds of millions of parameters!) that we struggle to understand the limits of such systems. Yet understanding networks becomes extremely important as networks are deployed in safety critical fields, like medicine and autonomous vehicles. Models must be vetted for robustness against adversarial examples, biases need to be identified and compensated for, and boundaries for what the network will produce need to be identified.

What MLC@Home Does

MLC@Home provides an open, collaborative platform for researchers studying machine learning comprehension. It allows us to train thousands of networks in parallel, with tightly controlled inputs, hyperparameters, and network structures. We use this to gain insights into these complex models.

MLC@Home's initial project, the Machine Learning Dataset Generator (MLDS), will generate a large dataset of simple networks trained with both clean and adversarial data. To our knowledge, this is the first dataset of its kind. MLC@Home also welcomes project proposals from other researchers aligned with this research area. MLC@Home requests that all data generated by our supported projects be made available to the public, and that, where possible, any papers and analysis be made public as well.

MLDS Live Status
NameCompleteIn ProgressTotal
SingleDirectMachine 9680 0 10004
EightBitMachine 9628 24 10006
SingleInvertMachine 9684 0 10003
SimpleXORMachine 9647 0 10002
ParityMachine 317 9354 10005
ParityModified 38 6309 10005
EightBitModified 980 5368 10006
SimpleXORModified 6345 1 10005
SingleDirectModified 6327 0 10004
SingleInvertModified 6393 0 10002
Last update: 2020-08-10 20:15:07.506563