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Feb, 2022
组合问题中输出空间不变性的神经模型
Neural Models for Output-Space Invariance in Combinatorial Problems
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Yatin Nandwani, Vidit Jain, Mausam, Parag Singla
TL;DR
本文介绍了一种基于递归关系网络(RNN)的方法来实现神经模型对于输出空间的不变性,进而解决在求解组合问题中出现的空间大小变化问题。我们提出的两种方法对于不同规模的问题都表现优异,并且存在一个权衡:二元模型在训练小范围的值集时表现更好,多元模型的内存效率更高,在训练大范围的值集时表现更好。
Abstract
Recently many
neural models
have been proposed to solve
combinatorial puzzles
by implicitly learning underlying constraints using their solved instances, such as sudoku or graph coloring (GCP). One drawback of th
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