Title :
Spurious Valleys in the Error Surface of Recurrent Networks—Analysis and Avoidance
Author :
Horn, Jason ; De Jesús, Orlando ; Hagan, Martin T.
Author_Institution :
Agilent Technol., High Freq. Technol. Center, Santa Clara, CA
fDate :
4/1/2009 12:00:00 AM
Abstract :
This paper gives a detailed analysis of the error surfaces of certain recurrent networks and explains some difficulties encountered in training recurrent networks. We show that these error surfaces contain many spurious valleys, and we analyze the mechanisms that cause the valleys to appear. We demonstrate that the principle mechanism can be understood through the analysis of the roots of random polynomials. This paper also provides suggestions for improvements in batch training procedures that can help avoid the difficulties caused by spurious valleys, thereby improving training speed and reliability.
Keywords :
backpropagation; error statistics; learning (artificial intelligence); recurrent neural nets; backpropagation; error surface; neural networks; random polynomial; recurrent network training; reliability; spurious valley; training speed; Backpropagation; error surface; recurrent neural networks; spurious minima; spurious valleys; training; Algorithms; Artificial Intelligence; Computer Simulation; Neural Networks (Computer); Nonlinear Dynamics;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2008.2012257