Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

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[6] Gal, Y., & Ghahramani, Z. (2016). Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. ICML. (The mathematical proof that keeping Dropout turned on during inference simulates a Bayesian network on standard GPUs).

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Randomness/Denoising,这一点在safew官方版本下载中也有详细论述

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[ITmedia N,更多细节参见wps下载

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