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Benefited from the merits of combining deep learning scheme with ensemble pruning paradigm, in the empirical results, DD-ELMs-ES demonstrates better generalization performance than the basic deep ELM models and some other state-of-the-art algorithms in tackling with time series forecasting tasks.
Liu and Wang [11] adopt a deep learning scheme to automatically learn the discriminative features from dense image patches.
DAE can effectively and flexibly learn mapping from corrupted speech to the original clean speech based on the deep learning scheme.
It is shown that networks with a deep structure can effectively and flexibly learn mapping from corrupted speech to the original clean speech based on the deep learning scheme.
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Enter the deep learning system.
Section 1.4 introduces the newest deep learning optimization schemes.
Because it demonstrated as an effective image content understand and tackle scheme, deep learning technology [59, 60], also known as deep neural networks, attracts growing interests in the fields of image and video search.
In this work, the mapping is done by the more general deep-learning scheme (DAE).
In this paper, a new class of topic modeling scheme called hybrid deep learning architecture is proposed for semantic image modeling and retrieval.
We couple this comparative scheme with a specific deep learning architecture, namely Region-Convolutional Neural Network (R-CNN) [13].
To this end, an undirected topic modeling scheme named as Hybrid Deep Learning Architecture (HDLA) is proposed and the latent topic-based image representation obtained with the proposed model yields semantically similar images in response to a given query.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com