Two-Level Softmax Sampling Done Right: Correcting Bias from Size Imbalance and Dispersion
Paper recorded by Signals 4 on 2026-10-07 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-07 on arXiv · recorded by Signals 4 on 2026-10-08
Category: cs.LG · 机器学习 · first seen 2026-10-08
Abstract
Sampling from a softmax distribution is a fundamental operation in machine learning, but its linear complexity in the number of items makes exact sampling impractical at scale. Two-level softmax (2LS) sampling is a popular alternative enabling sublinear-time sampling. Assuming items are partitioned into clusters, 2LS first samples a cluster and then an item within it. In this paper, we show that,
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Cite this page: Two-Level Softmax Sampling Done Right: Correcting Bias from Size Imbalance and Dispersion: the #337 most recent of 749 cs.LG papers we have recorded (as of 2026-10-07). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/two-level-softmax-sampling-done-right-correcting-bias-from-size-imbalance-and-di.html
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