Microsoft Technology Licensing, LLC

Pegah Ghahremani

James G Droppo

Michael Lewis Seltzer

Avner May

A neural network is structured with a plurality of levels of nodes. Each level has a level-specific stabilization parameter that adjusts a learning rate, at a corresponding level, during training. The stabilization parameter has a value that varies inversely relative to a change in an objective training function during back-propagation of the error through the level.

Self-stabilized deep neural network

A neural network is structured to connect the input values of an input set, at each level, to that level's output using a linear bypass connection. The linear bypass connection passes the input values, to the output, without applying a non-linear function to them.

Growing community of inventors

Baltimore, MD, United States of America

Pegah Ghahremani