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- W4387596087 abstract "Sorting is a fundamental operation of all computer systems, having been a long-standing significant research topic. Beyond the problem formulation of traditional sorting algorithms, we consider sorting problems for more abstract yet expressive inputs, e.g., multi-digit images and image fragments, through a neural sorting network. To learn a mapping from a high-dimensional input to an ordinal variable, the differentiability of sorting networks needs to be guaranteed. In this paper we define a softening error by a differentiable swap function, and develop an error-free swap function that holds non-decreasing and differentiability conditions. Furthermore, a permutation-equivariant Transformer network with multi-head attention is adopted to capture dependency between given inputs and also leverage its model capacity with self-attention. Experiments on diverse sorting benchmarks show that our methods perform better than or comparable to baseline methods." @default.
- W4387596087 created "2023-10-13" @default.
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- W4387596087 date "2023-10-10" @default.
- W4387596087 modified "2023-10-14" @default.
- W4387596087 title "Generalized Neural Sorting Networks with Error-Free Differentiable Swap Functions" @default.
- W4387596087 doi "https://doi.org/10.48550/arxiv.2310.07174" @default.
- W4387596087 hasPublicationYear "2023" @default.
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