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Workshop: AI for Science: from Theory to Practice

SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training

Kazem Meidani · Kazem Meidani · Parshin Shojaee · Parshin Shojaee · Chandan Reddy · Chandan Reddy · Amir Barati Farimani


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.

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