International Business Machines Corporation

Wisconsin Alumni Research Foundation

Tuan Dinh

Thanh Lam Hoang

Ngoc Minh Tran

Bradley Eck

Vikas Singh

Albert Akhriev

Vishnu Sai Rao Suresh Lokhande

Sathya Narayanan Ravi

An architecture for training the weights of artificial neural networks provides a global constrainer modifying the neuron weights in each iteration not only by the back-propagated error but also by a global constraint constraining these weights based on the value of all weights at that iteration. The ability to accommodate a global constraint is made practical by using a constrained gradient descent which approximates the error gradient deduced in the training as a plane, offsetting the increased complexity of the global constraint.

Training system for artificial neural networks having a global weight constrainer

Embodiments for intelligent unsupervised learning of visual alphabets by one or more processors are described. A visual three-dimensional (3D) alphabet may be learned from one or more images using a machine learning operations. A set of 3D primitives representing the visual 3D alphabet may be provided.

Unsupervised learning of three dimensional visual alphabet

Growing community of inventors

Madison, WI, United States of America

Tuan Dinh