Precision, Stability, and Generalization: A Comprehensive Assessment of RNNs learnability capability for Classifying Counter and Dyck Languages

This study investigates the learnability of Recurrent Neural Networks (RNNs) in classifying structured formal languages, focusing on counter and Dyck languages. Traditionally, both first-order (LSTM) and second-order (O2RNN) RNNs have been considered effective for such tasks, primarily based on thei...

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Veröffentlicht in:	arXiv.org 2024-10
Hauptverfasser:	Neisarg Dave, Kifer, Daniel, Lee, Giles, Mali, Ankur
Format:	Artikel
Sprache:	eng
Schlagworte:	Classification Data structures Languages Neural networks Recurrent neural networks Sampling methods Stability State machines Strings
Online-Zugang:	Volltext
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