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An $O(N)$ Sorting Algorithm: Machine Learning Sort

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arxiv 1805.04272 v2 pith:G4XRDQXS submitted 2018-05-11 cs.LG cs.DSstat.ML

classification cs.LGcs.DSstat.ML
keywords sortingalgorithmlearningmachineaccelerationapplicationappliedcdot
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abstract

We propose an $O(N\cdot M)$ sorting algorithm by Machine Learning method, which shows a huge potential sorting big data. This sorting algorithm can be applied to parallel sorting and is suitable for GPU or TPU acceleration. Furthermore, we discuss the application of this algorithm to sparse hash table.

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Cited by 1 Pith paper

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  1. EZ-Sort: Efficient Pairwise Comparison via Zero-Shot CLIP-Based Pre-Ordering and Human-in-the-Loop Sorting

    cs.CV 2025-08 conditional novelty 6.0 of 10

    Zero-shot CLIP pre-ordering plus uncertainty-guided MergeSort reduces human pairwise-comparison annotations by up to 90.5% and by 19.8% over prior active sorting.

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