Bridging the Empirical-Theoretical Gap in Neural Network Formal Language Learning Using Minimum Description Length

Autor:	Lan, Nur, Chemla, Emmanuel, Katzir, Roni
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Formal Languages and Automata Theory
Druh dokumentu:	Working Paper
Popis:	Neural networks offer good approximation to many tasks but consistently fail to reach perfect generalization, even when theoretical work shows that such perfect solutions can be expressed by certain architectures. Using the task of formal language learning, we focus on one simple formal language and show that the theoretically correct solution is in fact not an optimum of commonly used objectives -- even with regularization techniques that according to common wisdom should lead to simple weights and good generalization (L1, L2) or other meta-heuristics (early-stopping, dropout). On the other hand, replacing standard targets with the Minimum Description Length objective (MDL) results in the correct solution being an optimum. Comment: 9 pages, 5 figures, 3 appendix pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.10013 Zobrazit plný text záznamu View this record from Arxiv