PROVISIONING DEEP LEARNING (DL) MODELS THAT PRESERVE RELATIONSHIPS BETWEEN RESPONSE VARIABLES AND SELECTED EXPLANATORY VARIABLES

PROVISIONING DEEP LEARNING (DL) MODELS THAT PRESERVE RELATIONSHIPS BETWEEN RESPONSE VARIABLES AND SELECTED EXPLANATORY VARIABLES

Implementations for training a denoising stacked autoencoder (DAE) using a noisy training dataset comprising a noisy sub-set and a non-noisy sub-set, providing an artificial neural network (ANN) including multiple hidden layers, at least one hidden layer including at least a portion of an encoder of...

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Bibliographische Detailangaben
Hauptverfasser: Chate, Sneha, Gaba, Rajan, Rajendran, Boobesh, Chopra, Neha, Tyagi, Shiva, Passalis, Georgios
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Beschreibung
Zusammenfassung:Implementations for training a denoising stacked autoencoder (DAE) using a noisy training dataset comprising a noisy sub-set and a non-noisy sub-set, providing an artificial neural network (ANN) including multiple hidden layers, at least one hidden layer including at least a portion of an encoder of the DAE, the at least a portion of the encoder comprising parameters determined during training of the DAE, training the ANN using a training dataset, and providing a version of the ANN for inference.