Abstract
We propose two output activation functions for estimating probability distributions over an unbounded number of categories with a recurrent neural network, and derive the statistical assumptions which they embody. Both these methods perform better than the standard approach to such problems, when applied to probabilistic parsing of natural language with Simple Synchrony Networks.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
