analiza aiqbench
CAPSkip is a training-efficiency method that skips backpropagation for some correctly classified examples in each mini-batch, choosing them so their combined last-layer gradient stays close to the full batch's. The paper's guarantee is narrow: it bounds the change only in the raw final-layer gradient, and not in deeper layers, optimizer updates, or the training trajectory. The reported results (0.576x the time of dense training on DeiT-S/CIFAR-100, with a small accuracy gain over a time-matched dense baseline, n=3) are promising but come from one model and dataset, so readers should wait for broader validation before treating the speedup as general.
CAPSkip: Cancellation-Aware Prefix Skipping for Selective Backpropagation
arXiv:2611.04611v1 Announce Type: new Abstract: Per-step selective back-propagation forwards every example of a mini-batch but back-propagates only some of them. Most rules rank examples by loss or gradient magnitude, but such scalar scores ignore cancellation between per-example gradients: the gradient removed by a set can be much smaller than its individual magnitudes suggest. We introduce CAPSkip (Cancellation-Aware Prefix Skipping). Among the examples the model already classifies correctly, CAPSkip skips the longest prefix, in increasing order of last-layer gradient norm, whose vector sum stays within a relative budget $b$ of the batch's last-layer gradient. In exact arithmetic this bounds the change of the raw final-layer gradient, using rank-one contributions already available from the forward pass; the bound does not extend to deeper layers, optimizer updates, or the training trajectory. Because the cumulative distortion is not monotone in the prefix length, CAPSkip scans all prefixes; a factorised Gram matrix makes the scan cheap and keeps it on the device. With activation reuse and selected-row backward, CAPSkip fine-tunes DeiT-S on CIFAR-100 for 103 epochs in 0.576x the time of 103-epoch dense training, and is +0.25 pp [+0.08, +0.42] above an approximately time-matched 59-epoch dense run (n=3).
Abstract:Per-step selective back-propagation forwards every example of a mini-batch but back-propagates only some of them. Most rules rank examples by loss or gradient magnitude, but such scalar scores ignore cancellation between per-example gradients: the gradient removed by a set can be much smaller than its individual magnitudes suggest. We introduce CAPSkip (Cancellation-Aware Prefix Skipping). Among the examples the model already classifies correctly, CAPSkip skips the longest prefix, in increasing order of last-layer gradient norm, whose vector sum stays within a relative budget $b$ of the batch's last-layer gradient. In exact arithmetic this bounds the change of the raw final-layer gradient, using rank-one contributions already available from the forward pass; the bound does not extend to deeper layers, optimizer updates, or the training trajectory. Because the cumulative distortion is not monotone in the prefix length, CAPSkip scans all prefixes; a factorised Gram matrix makes the scan cheap and keeps it on the device. With activation reuse and selected-row backward, CAPSkip fine-tunes DeiT-S on CIFAR-100 for 103 epochs in 0.576x the time of 103-epoch dense training, and is +0.25 pp [+0.08, +0.42] above an approximately time-matched 59-epoch dense run (n=3).
Machine Learning (cs.LG)
arXiv:2611.04611 [cs.LG]
(or arXiv:2611.04611v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2611.04611 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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