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The saving pruning offers is only ever at inference, because the sparse architectures it produces are hard to train from the start.

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.

What they actually said

  1. Michael Carbin Associate professor at MIT However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance. Frankle & Carbin, "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" (arXiv)arxiv.org · 9 Mar 2018All 10 korrents from this piece
    Their wordsHowever, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance.

    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 4th

  2. Jonathan Frankle Chief AI scientist at Databricks However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance. Frankle & Carbin, "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" (arXiv)arxiv.org · 9 Mar 2018All 10 korrents from this piece
    Their wordsHowever, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance.

    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 3rd

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