Titlebook: Human Brain and Artificial Intelligence; First International An Zeng,Dan Pan,Xiaowei Song Conference proceedings 2019 Springer Nature Sing

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EEGNAS: Neural Architecture Search for Electroencephalography Data Analysis and Decodingenges related to EEG: (1) small amounts of labeled EEG data per subject, and (2) high diversity of EEG signal patterns across subjects. Neural network architectures produced during this study successfully compete with state of the art architectures published in the literature. Particularly successfu

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Multi-task Dictionary Learning Based on Convolutional Neural Networks for Longitudinal Clinical Scororphometry statistics (MMS). We applied the novel CNN-MSCC system on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to predict future cognitive clinical measures with baseline Hippocampal/Ventricle MMS features and cortical thickness. The experimental results showed that CNN-MSCC ach

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A Robust Automated Pipeline for Localizing SEEG Electrode Contactsances, interconnected electrodes determination and separation (IEDS), and craniocerebral interference removing (CCIR). The robustness and generality of our algorithm was validated on 12 subjects (135 electrodes, 1812 contacts). Compared to the manual segmentation (240 contacts), automatic localizati

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Task-Nonspecific and Modality-Nonspecific AIensory modalities used the same DN learning engine, but each had a different body (sensors and effectors). The contestants independently verified the DN’s base performance, and competed to add (hinted) autonomous attention for better performance. This seems to be the first task-independent and modal

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Brain Research and Arbitrary Multiscale Quantum Uncertaintyy advanced deep learning and deep thinking systems, we need a unified, integrated, convenient, and universal representation framework, by considering information not only on the statistical manifold of model states, but also on the combinatorical manifold of low-level discrete, directed energy gener

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Learning Preferences in a Cognitive Decision Model preferences compatible with the observed choice behavior and, thus, provides a method for learning a rich preference model of an individual which encompasses psychological aspects and which can be used as more realistic predictor of future behavior.