Jump to content

Backpropagation through structure

From Wikipedia, the free encyclopedia
This is an old revision of this page, as edited by Olexa Riznyk (talk | contribs) at 17:59, 16 April 2017 (Adding wikilink(s), adding external link(s), +stub). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Backpropagation Through Structure (BPTS) is a gradient-based technique for training recursive neural nets (a superset of recurrent neural nets) and is extensively described in a 1996 paper written by Christoph Goller and Andreas Küchler.[1]

References

  1. ^ Kuchler, Andreas. "Learning Task-Dependent Distributed Representations by Backpropagation Through Structure" (PDF). psu.edu. CiteSeerX 10.1.1.49.1968.
Stub icon

This artificial intelligence-related article is a stub. You can help Wikipedia by expanding it.

Retrieved from "https://en.wikipedia.org/w/index.php?title=Backpropagation_through_structure&oldid=775722895"