Detection of Voltage Anomalies in Spacecraft Storage Batteries Based on a Deep Belief Network. [PDF]
Sensors (Basel), 2019 Li X, Zhang T, Liu Y.
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Deep Belief Network for Spectral⁻Spatial Classification of Hyperspectral Remote Sensor Data. [PDF]
Sensors (Basel), 2019 Li C +5 more
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Discovering hierarchical common brain networks via multimodal deep belief network. [PDF]
Med Image Anal, 2019 Zhang S +5 more
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A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network. [PDF]
Front Neurosci, 2018 Chu Y +5 more
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Automated classification of dense calcium tissues in gray-scale intravascular ultrasound images using a deep belief network. [PDF]
BMC Med Imaging, 2019 Lee J +4 more
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A new method for enhancer prediction based on deep belief network. [PDF]
BMC Bioinformatics, 2017 Bu H, Gan Y, Wang Y, Zhou S, Guan J.
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Convolutional Deep Belief Network with Feature Encoding for Classification of Neuroblastoma Histological Images. [PDF]
J Pathol Inform, 2018 Gheisari S +3 more
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Deep neural network in QSAR studies using deep belief network
Applied Soft Computing Journal, 2018 Abstract There are two major challenges in the current high throughput screening drug design: the large number of descriptors which may also have autocorrelations and, proper parameter initialization in model prediction to avoid over-fitting problem.
Fahimeh Ghasemi +2 more
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Deep Recurrent Belief Propagation Network for POMDPs
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 In many real-world sequential decision-making tasks, especially in continuous control like robotic control, it is rare that the observations are perfect, that is, the sensory data could be incomplete, noisy or even dynamically polluted due to the unexpected malfunctions or intrinsic low quality of the sensors.
Yuhui Wang 0004, Xiaoyang Tan
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Sequential Deep Belief Networks
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012Previous work applying Deep Belief Networks (DBNs) to problems in speech processing has combined the output of a DBN trained over a sliding window of input with an HMM or CRF to model linear-chain dependencies in the output. We describe a new model called Sequential DBN (SDBN) that uses inherently sequential models in all hidden layers as well as in ...
Galen Andrew, Jeff A. Bilmes
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