mlsanityData to 7 Oct 2026

Generating Sequences With Recurrent Neural Networks

arXiv 1308.0850 ↗First posted 4 Aug 2013cs.NE cs.CLcited by 549

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Cited by 32 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.

Key facts. Cited by 549 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Aug 2013.

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Citing papers per year: 2019: 1; 2020: 1; 2021: 142; 2022: 102; 2023: 106; 2024: 85; 2025: 77; 2026: 35.

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