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1 de dic. de 2023 · Here, we studied the deep reduction-hydrogen production characteristics. Specifically, the effects of temperature, papermaking sludge (PMS) blended ratio, and reaction time on the deep reduction characteristics of Fe in pickling sludge and presintered waste were studied.
1 de ene. de 2019 · Dimensionality Reduction (DR) is the pre-processing step to remove redundant features, noisy and irrelevant data, in order to improve learning feature accuracy and reduce the training time. Dimensionality reductions techniques have been proposed and implemented by using feature selection and extraction method.
17 de may. de 2021 · Vogelstein et al. propose a supervised dimensionality reduction method which estimates the low-dimensional data projection for classification and prediction in big datasets.
1 de mar. de 2024 · This study introduces a novel approach called deep mechanism reduction (DeePMR). DeePMR focuses on constructing a mapping function between reduced mechanisms and their overall performance using deep neural networks, so that DeePMR can gradually eliminate species and associated reactions while ensuring accurate functional preservation.
10 de jun. de 2020 · We propose a deep dimension reduction approach to learning representations with these characteristics. The proposed approach is a nonparametric generalization of the sufficient dimension reduction method.
4 de feb. de 2020 · In this paper, we demonstrate a computationally efficient new approach based on deep learning (DL) techniques for analysis, design and optimization of electromagnetic (EM) nanostructures.
9 de dic. de 2020 · In a nutshell, an autoencoder is a neural network based model to compress the data. Therefore, it has the ability to learn the compressed representation of our input data. In the early development of Deep Learning, autoencoder has been viewed as a solution to solve the problem of unsupervised learning.
