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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

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.

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What they actually said

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

  1. Michael Carbin 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. arxiv.org

    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.
  2. Jonathan Frankle 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. arxiv.org

    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.

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

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