oroikono/sigs-icml-2026
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Datasets:
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End of preview. Expand in Data Studio
SIGS Grammar Production Corpus
This dataset contains the one-hot grammar-production sequences used to train the Grammar-VAE in SIGS: Neuro-Symbolic AI for Analytical Solutions of Differential Equations (Oikonomou et al., ICML 2026).
Structure
Each row contains:
inputs: a float32 tensor shaped[grammar productions, sequence length];labels: the corresponding integer production indices shaped[sequence length].
The deterministic default split uses seed 42 with 70% training, 20% validation, and 10% test examples.
Intended use
- Training or evaluating grammar-guided symbolic generative models.
- Reproducing the SIGS Grammar-VAE representation-learning stage.
- Studying structured representations for symbolic differential equations.
Limitations
The corpus reflects the published SIGS grammar and expression-generation procedure. It should not be treated as representative of all valid mathematical expressions or differential equations. Models trained on it inherit its grammar, maximum-length, and sampling biases.
Citation
@misc{oikonomou2026neurosymbolic,
title={Neuro-Symbolic AI for Analytical Solutions of Differential Equations},
author={Oikonomou, Orestis and Lingsch, Levi and Grund, Dana and Mishra, Siddhartha and Kissas, Georgios},
year={2026},
eprint={2502.01476},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
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