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This research paper presents a numerical study of four optimization algorithms for training deep neural networks, inspired by the application of partial differential equations. The focus is on comparing the performance of these algorithms, namely ESGD, H-ESGD, HEAT, and HJ, against the conventional stochastic gradient descent method, under various conditions and on different datasets. The results hint at improvements in training efficiency under certain circumstances, emphasizing the role of...
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Information reported in a numerical study typically includes data sets, statistics, analysis results, methodologies used, and conclusions drawn from the analysis.
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