Sequential Neural Networks as Automata
This work provides theoretical insights to increase understanding and interpretation of neural networks, particularly for researchers in machine learning and computational linguistics, but it is incremental as it builds on existing automata theory.
The paper tackles the problem of explaining the computational capabilities of neural networks by relating them to automata, characterizing language classes for recurrent, attention, and convolutional networks, and finding that LSTMs function like counter machines and convolutional networks relate to the subregular hierarchy.
This work attempts to explain the types of computation that neural networks can perform by relating them to automata. We first define what it means for a real-time network with bounded precision to accept a language. A measure of network memory follows from this definition. We then characterize the classes of languages acceptable by various recurrent networks, attention, and convolutional networks. We find that LSTMs function like counter machines and relate convolutional networks to the subregular hierarchy. Overall, this work attempts to increase our understanding and ability to interpret neural networks through the lens of theory. These theoretical insights help explain neural computation, as well as the relationship between neural networks and natural language grammar.