Brainnetome center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China;
University of Chinese Academy of Sciences, Beijing, China;
The Mind Research Network/LBERI, Albuquerque, NM, USA;
Brainnetome center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China;
Brainnetome center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China;
Brainnetome center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China;
Brainnetome center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China;
Training; Integrated circuits; Neurons; Biological neural networks; Machine learning; Learning systems;
机译:具有权重稀疏控制和预训练的深度神经网络可提取分层特征并增强分类性能:来自精神分裂症的全脑静止状态功能连接模式的证据
机译:双相情感障碍和精神分裂症中五个神经网络的静止状态功能连接
机译:使用新型特征选择方法使用深层神经网络从大脑静止状态功能连接性模式诊断自闭症谱系障碍
机译:使用静态状态功能网络连接来区分从正常控制的精神分裂症:深神经网络和层面相关传播方法
机译:克拉姆网:层面深度神经网络压缩,具有来自教师网络的知识转移
机译:分层明智相关性传播用于解释基于MRI的阿尔茨海默氏病分类中的深层神经网络决策
机译:具有重量稀疏控制和预训练提取的深神经网络分层特征和增强分类性能:来自精神分裂症的全脑休息状态功能连通性模式的证据