What Michael Carbin thinks about neural networks

@michael-carbin · 5 positions · On ppll ↗

Associate professor at MIT, where he leads the Programming Systems Group, and co-author of the lottery ticket hypothesis.

Michael Carbin did not write this page.

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Only Michael Carbin on neural networks. See everyone on neural networks

4 dated positions, 2018, in their own words. Our reading of what Michael Carbin has said — not written or endorsed by them.

4 positions so far — this page is not yet offered to search engines.

1. 9 Mar 2018

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

   pruning

   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.

   ↗Frankle & Carbin, "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" (arXiv)arxiv.org All 10 from this paper

3. 9 Mar 2018

   Our readingPruning does not merely shrink a network; it uncovers subnetworks whose original initialization was what made them trainable at all.

   pruning

   Their wordsWe 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 All 10 from this paper

5. 9 Mar 2018

   Our readingA winning ticket wins on its initial weights: the connections it keeps started at values that happen to make training work.

   pruning

   Their wordsThe winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective.

   ↗Frankle & Carbin, "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" (arXiv)arxiv.org All 10 from this paper

7. 9 Mar 2018

   Our readingA small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it.

   pruning

   Their wordsAbove this size, the winning tickets that we find learn faster than the original network and reach higher test accuracy.

   ↗Frankle & Carbin, "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" (arXiv)arxiv.org All 10 from this paper