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Pruning cuts the parameters of a trained network by over 90% without costing accuracy, so inference gets smaller and faster for free.

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 Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. Frankle & Carbin, "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" (arXiv)arxiv.org · 9 Mar 2018All 10 korrents from this piece
    Their wordsNeural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy.

    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 2nd

  2. Jonathan Frankle Chief AI scientist at Databricks Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. Frankle & Carbin, "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" (arXiv)arxiv.org · 9 Mar 2018All 10 korrents from this piece
    Their wordsNeural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy.

    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 1st

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