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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

pruning
What this subject means

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

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What they actually said

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  1. Michael Carbin However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance. arxiv.org

    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.
  2. Jonathan Frankle However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance. arxiv.org

    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.

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

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