Accession Number:

AD1028510

Title:

Implicity Defined Neural Networks for Sequence Labeling

Descriptive Note:

Technical Report

Corporate Author:

MASSACHUSETTS INST OF TECH LEXINGTON LEXINGTON United States

Personal Author(s):

Report Date:

2017-02-13

Pagination or Media Count:

5.0

Abstract:

In this work, we propose a novel, implicitly defined neural network architecture and describe a method to compute its components. The proposed architecture forgoes the causality assumption previously used to formulate recurrent neural networks and allow the hidden states of the network to coupled together, allowing potential improvement on problems with complex, long-distance dependencies. Initial experiments demonstrate the new architecture outperforms both the Stanford Parser and a baseline bidirectional network on the Penn Treebank Part-of-Speech tagging task and a baseline bidirectional network on an additional artificial random biased walk task.

Subject Categories:

Distribution Statement:

APPROVED FOR PUBLIC RELEASE