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Pruning does not merely shrink a network; it uncovers subnetworks whose original initialization was what made them trainable at all.

Drawn from what Michael Carbin and Jonathan Frankle said

Subjectpruning
What this subject means

pruning Cutting connections that are not doing the work — in a trained network, and in a developing brain.

Interesting
A private bookmark. Not a position, and never counted.

What they actually said

Word for word, with the source under each one. They did not write this page.

  1. Michael Carbin We find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively. arxiv.org

    Associate professor at MIT

    Their wordsWe find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively.

    Where this was said

    ↗Frankle & Carbin, "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" (arXiv)arxiv.org

    All 10 korrents from this piecethis one is 6th

  2. Jonathan Frankle We find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively. arxiv.org

    Chief AI scientist at Databricks

    Their wordsWe find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively.

    Where this was said

    ↗Frankle & Carbin, "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" (arXiv)arxiv.org

    All 10 korrents from this piecethis one is 5th

Added to korrents 9 Mar 2018 · How quotes work · Something wrong? Tell us

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