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Successive Halving Top-k Operator (2010.15552v1)
Published 8 Oct 2020 in cs.LG
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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