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arXiv · 2010.15552

Successive Halving Top-k Operator

Michał Pietruszka·Łukasz Borchmann·Filip Graliński
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Abstract

We propose a differentiable successive halving method of relaxing the top-k operator, rendering gradient-based optimization possible. The need to perform softmax iteratively on the entire vector of scores is avoided by using a tournament-style selection. As a result, a much better approximation of top-k with lower computational cost is achieved compared to the previous approach.

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Michał Pietruszka, Łukasz Borchmann, Filip Graliński. 2020-10-08. Successive Halving Top-k Operator. https://arxiv.org/abs/2010.15552

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