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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

Subjectsneural networkspruning
What these subjects mean

neural networks The architecture almost all modern machine learning is built on, and what changes when it gets deeper.

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

What they actually said

  1. Michael Carbin Associate professor at MIT We find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively. Frankle & Carbin, "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" (arXiv)arxiv.org · 9 Mar 2018All 10 korrents from this piece
    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

    9 Mar 2018

    All 10 korrents from this piecethis one is 6th

  2. Jonathan Frankle Chief AI scientist at Databricks We find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively. Frankle & Carbin, "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" (arXiv)arxiv.org · 9 Mar 2018All 10 korrents from this piece
    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

    9 Mar 2018

    All 10 korrents from this piecethis one is 5th

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