Multi-Operational Mathematical Derivations in Latent Space

arXiv:2311.01230v2 Announce Type: replace
Abstract: This paper investigates the possibility of approximating multiple mathematical operations in latent space for expression derivation. To this end, we introduce different multi-operational representation paradigms, modelling mathematical operations as explicit geometric transformations. By leveraging a symbolic engine, we construct a large-scale dataset comprising 1.7M derivation steps stemming from 61K premises and 6 operators, analysing the properties of each paradigm when instantiated with state-of-the-art neural encoders. Specifically, we investigate how different encoding mechanisms can approximate expression …

abstract arxiv construct cs.ai cs.lg cs.sc dataset derivation modelling multiple operations paper possibility representation scale space stemming type