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US Patent 10255529 Structure learning in convolutional neural networks

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Is a
Patent
Patent

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10255529
Date of Patent
April 9, 2019
Patent Application Number
15457990
Date Filed
March 13, 2017
Patent Citations Received
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US Patent 11657286 Structure learning in convolutional neural networks
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US Patent 11775841 Process and system including explainable prescriptions through surrogate-assisted evolution
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US Patent 11669716 System and method for implementing modular universal reparameterization for deep multi-task learning across diverse domains
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US Patent 11783195 Process and system including an optimization engine with evolutionary surrogate-assisted prescriptions
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US Patent 11775836 Hand pose estimation
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US Patent 10977548 Generation of capsule neural networks for enhancing image processing platforms
Patent Primary Examiner
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Hadi Akhavannik
Patent abstract

The present disclosure provides an improved approach to implement structure learning of neural networks by exploiting correlations in the data/problem the networks aim to solve. A greedy approach is described that finds bottlenecks of information gain from the bottom convolutional layers all the way to the fully connected layers. Rather than simply making the architecture deeper, additional computation and capacitance is only added where it is required.

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