FREEZE-OUT AS A REGULARIZER IN TRAINING NEURAL NETWORKS

Systems and techniques that facilitate freeze-out as a regularizer in training neural networks are presented. A system can include a memory and a processor that executes computer executable components. The computer executable components can include: an assessment component that identifies units of a...

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Bibliographic Details
Main Authors Zhang, Min, Avinash, Gopal Biligeri, Török, Levente Imre, Tan, Tao, Ferenczi, Lehel, Tegzes, Pál
Format Patent
LanguageEnglish
Published 29.07.2021
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:Systems and techniques that facilitate freeze-out as a regularizer in training neural networks are presented. A system can include a memory and a processor that executes computer executable components. The computer executable components can include: an assessment component that identifies units of a neural network, a selection component that selects a subset of units of the neural network, and a freeze-out component that freezes the selected subset of units of the neural network so that weights of output connections from the frozen subset of units will not be updated for a training run.
Bibliography:Application Number: US202016773156