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Research map / Learning theory and algorithms / Combinatorial and search optimization

Learning To Solve Hard Minimal Problems

CVPR 2022 · Learning theory and algorithms › Combinatorial and search optimization · approach: Unsupervised neural combinatorial optimization · 30 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the learned anchor classifier instead of the nearest-anchor Mahalanobis baseline5pt solver with A75 · 5pt problemsuccess rate (%): 13.4 -> 29.0Helped
Use the learned anchor classifier instead of the nearest-anchor Mahalanobis baseline5pt solver with A75 · 5pt problemeffective solving time (μs): 95.3 -> 26.1Helped
Use the learned anchor classifier instead of the nearest-anchor Euclidean baseline4pt solver with A90 · 4pt problemsuccess rate (%): 5.0 -> 26.3Helped
Use the learned anchor classifier instead of the nearest-anchor Euclidean baseline4pt solver with A90 · 4pt problemeffective solving time (μs): 324.5 -> 61.6Helped
Add the TRASH output class (ablation)5pt classifier with A90 · 5pt problemsuccess rate (%): 39.9 -> 36.8Hurt
Add the TRASH output class (ablation)4pt classifier with A90 · 4pt problemsuccess rate (%): 29.2 -> 26.3Hurt
Use anchors from Office and Terrains instead of Office aloneanchor selection · Testing p-s problems from six ETH 3D modelscoverage (%): 81.1 -> 82.2Helped
Increase the number of anchors from 8 to 5855pt anchor set · delivery_areaproblem coverage (%): 43.3 -> 96.5Helped
Increase the number of anchors from 16 to 12054pt anchor set · delivery_areaproblem coverage (%): 47.4 -> 96.6Helped

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