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Multi-Operational Mathematical Derivations in Latent Space. (arXiv:2311.01230v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
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
equational reasoning in latent space, exploring …
arxiv construct dataset derivation modelling multiple operations paper representation scale space stemming