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

arXiv:2402.10013v1 Announce Type: new
Abstract: 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 …

abstract approximation architectures arxiv cs.cl cs.fl focus gap good language network networks neural network neural networks shows solutions tasks type work