Michael Carbin
Associate Professor at MIT CSAIL, leading the Programming Systems Group; works on programming systems that manipulate uncertainty, from neural networks to unreliable hardware.
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What they believe
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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.
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.orgSaid 9 Mar 2018This on korrents
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Beliefs
Korrents What they believe 5 beliefs — each backed by an exact quote.
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Recent
Pruning cuts the parameters of a trained network by over 90% without costing accuracy, so inference gets smaller and faster for free.
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) Said 9 Mar 2018
The saving pruning offers is only ever at inference, because the sparse architectures it produces are hard to train from the start.
However, 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) Said 9 Mar 2018
Pruning does not merely shrink a network; it uncovers subnetworks whose original initialization was what made them trainable at all.
We 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) Said 9 Mar 2018
Show 2 more
A winning ticket wins on its initial weights: the connections it keeps started at values that happen to make training work.
The 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) Said 9 Mar 2018
A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it.
Above 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) Said 9 Mar 2018
Beliefs others hold too
Pruning cuts the parameters of a trained network by over 90% without costing accuracy, so inference gets smaller and faster for free. 2 hold this
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) Said 9 Mar 2018
The saving pruning offers is only ever at inference, because the sparse architectures it produces are hard to train from the start. 2 hold this
However, 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) Said 9 Mar 2018
Pruning does not merely shrink a network; it uncovers subnetworks whose original initialization was what made them trainable at all. 2 hold this
We 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) Said 9 Mar 2018
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Where they publish
LinkedIn Michael Carbin His professional profile.
Career history and publications, kept current alongside his MIT page.
Link verified 9 Sept 2026.
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