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

arXiv:2310.02227v3 Announce Type: replace
Abstract: In an era where symbolic mathematical equations are indispensable for modeling complex natural phenomena, scientific inquiry often involves collecting observations and translating them into mathematical expressions. Recently, deep learning has emerged as a powerful tool for extracting insights from data. However, existing models typically specialize in either numeric or symbolic domains, and are usually trained in a supervised manner tailored to specific tasks. This approach neglects the substantial benefits that could arise from a task-agnostic …

arxiv cs.ai cs.lg pre-training training type