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Cutting edge topics: distributed asynchronous learning

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If you have a question about this talk, please contact Damon Wischik.

For models that involve structured input e.g. a different graph structure for each data instance, the standard deep learning technology is inefficient. This talk will describe Asynchronous Model-Parallel (AMP) training, in which multiple cores or devices each learn different parts of the network asynchronously, which makes much more efficient use of hardware.

This talk is part of the Mathematics and Machine Learning series.

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