Accelerated Gradient Descent Using Instance Eliminating Back Propagation

Farzad Hosseinali

doi:10.20944/preprints202008.0181.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 6 August 2020 / Approved: 7 August 2020 / Online: 7 August 2020 (09:29:54 CEST)

Artificial Intelligence is dominated by Artificial Neural Networks (ANNs). Currently, the Batch Gradient Descent (BGD) is the only solution to train ANN weights when dealing with large datasets. In this article, a modification to the BGD is proposed which significantly reduces the training time and improves the convergence. The modification, called Instance Eliminating Back Propagation (IEBP), eliminates correctly-predicted-instances from the Back Propagation. The speedup is due to the elimination of unnecessary matrix multiplication operations from the Back Propagation. The proposed modification does not add any training hyperparameter to the existing ones and reduces the memory consumption during the training.

Artificial Neural Networks; Gradient Descent; Back Propagation; Instance Elimination; Speed up; Batch Size

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Accelerated Gradient Descent Using Instance Eliminating Back Propagation

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