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Research map / Learning theory and algorithms / Optimal transport

Monotonic Differentiable Sorting Networks

ICLR 2022 · Learning theory and algorithms › Optimal transport · approach: Neural optimal transport · 0 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace logistic sigmoid with monotonic sigmoid relaxationsDifferentiable sorting networks · MNIST n=15full rankings correctly identified: 23.5 -> 31.8Helped
Replace logistic sigmoid with monotonic sigmoid relaxationsDifferentiable sorting networks · SVHN n=15full rankings correctly identified: 2.4 -> 13.7Helped
Use optimal sigmoid rather than Cauchy CDF (ablation)Differentiable sorting networks · MNIST odd-even n=15full rankings correctly identified: 31.1 -> 31.8Helped
Use Cauchy CDF rather than optimal sigmoid (ablation)Differentiable sorting networks · MNIST bitonic n=16full rankings correctly identified: 28.4 -> 29.0Helped
Use Cauchy CDF rather than optimal sigmoid (ablation)Differentiable sorting networks · MNIST bitonic n=32full rankings correctly identified: 1.4 -> 1.6Helped
Use optimal sigmoid rather than Cauchy CDF (ablation)Differentiable sorting networks · MNIST odd-even n=32full rankings correctly identified: 0.8 -> 1.4Helped
Use Cauchy CDF rather than optimal sigmoid (ablation)Differentiable sorting networks · SVHN bitonic n=16full rankings correctly identified: 10.6 -> 12.2Helped
Replace the logistic sigmoid with the reciprocal sigmoid (ablation)Differentiable sorting networks · MNIST n=3full rankings correctly identified: 94.3 -> 94.4No clear effect
Tune inverse temperature for the monotonic sigmoid (ablation)Monotonic differentiable sorting networks · MNIST sorting tasksindividual element ranks correctly identifiedHelped

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