May 14, 2024, 4:42 a.m. | Danyang Li, Mingyu Cai, Cristian-Ioan Vasile, Roberto Tron

cs.LG updates on

arXiv:2405.06670v1 Announce Type: cross
Abstract: There has been a growing interest in extracting formal descriptions of the system behaviors from data. Signal Temporal Logic (STL) is an expressive formal language used to describe spatial-temporal properties with interpretability. This paper introduces TLINet, a neural-symbolic framework for learning STL formulas. The computation in TLINet is differentiable, enabling the usage of off-the-shelf gradient-based tools during the learning process. In contrast to existing approaches, we introduce approximation methods for max operator designed specifically for …

abstract arxiv computation cs.lg cs.lo data differentiable framework inference interpretability language logic network neural network paper signal spatial stl temporal type

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