@inproceedings{
meidani2023snip,
title={{SNIP}: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training},
author={Kazem Meidani and Parshin Shojaee and Chandan Reddy and Amir Barati Farimani},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=Nn43zREWvX}
}
In scientific inquiry, symbolic mathematical equations play a fundamental role in modeling complex natural phenomena. Leveraging the power of deep learning, we introduce SNIP, a Multi-Modal Symbolic-Numeric Pre-training framework. By employing joint contrastive learning between symbolic and numeric domains, SNIP enhances their mutual alignment in pre-trained embeddings. Latent space analysis reveals that symbolic supervision significantly enriches the embeddings of numeric data, and vice versa. Evaluations across diverse tasks, including symbolic-to-numeric and numeric-to-symbolic property prediction, demonstrate SNIP's superior performance over fully supervised baselines. This advantage is particularly pronounced in few-shot learning scenarios, making SNIP a valuable asset in situations with limited available data.
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training
Kazem Meidani Parshin Shojaee Chandan K. Reddy Amir Barati Farimani