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Deep architecture structures have been recommended to predict the compounds biological activity.
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Deep architecture and recurrent structures are employed in the SSREDNs to model both the complex nonlinear mapping relationship between input protein features and SS, and the mutual interaction among continuous residues of the protein chain.
The deep architecture is regarded as being similar to the hierarchical structures within human visual and auditory systems, where the raw image or speech waveforms are transformed to a high-order linguistic level by these hierarchical structures [15 18].
Meanwhile, CNN is easily prone to overfitting with deep architecture.
The supervised information and the deep architecture are collaboratively explored.
Let F denotes the deep architecture function.
Its deep architecture nature grants deep learning the possibility of solving much more complicated AI tasks (Bengio, [42]) [2].
Bengio, Y. Learning Deep Architectures for AI.
In music and architecture, structure displaces surface as the zone of emphasis.
Deep learning algorithms are actually Deep architectures of consecutive layers.
We demonstrate that deep architectures can be beneficial even with a sparse biological dataset.
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