korrents

Michael Carbin

@michael-carbin · 5 positions · 0 changes of mind

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

Everything they publish, on ppll ↗

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  1. Our reading

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

    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 2nd of 10 in this piece

    neural networkspruning

    Interesting

  3. Our reading

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

    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.

    ↗Frankle & Carbin, "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" (arXiv)arxiv.org 4th of 10 in this piece

    pruning

    Interesting

  5. Our reading

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

    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 6th of 10 in this piece

    pruning

    Interesting

  7. Our reading

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

    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 8th of 10 in this piece

    pruning

    Interesting

  9. Our reading

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

    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 10th of 10 in this piece

    pruning

    Interesting