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机译:无监督卷积自动编码网络和监督卷积神经网络的联合应用数据驱动矿物前瞻性映射
China Univ Geosci Beijing Sch Earth Sci &
Resources 29 Xueyuan Rd Beijing 100083 Peoples R China;
Univ KwaZulu Natal Sch Agr Earth &
Environm Sci Westville Campus Durban South Africa;
Chinese Acad Geol Sci Inst Mineral Resources MIR Key Lab Metallogeny &
Mineral Resource Assess Beijing 100037 Peoples R China;
Chinese Acad Geol Sci Inst Mineral Resources MIR Key Lab Metallogeny &
Mineral Resource Assess Beijing 100037 Peoples R China;
China Univ Geosci Beijing Sch Earth Sci &
Resources 29 Xueyuan Rd Beijing 100083 Peoples R China;
Chinese Acad Geol Sci Inst Mineral Resources MIR Key Lab Metallogeny &
Mineral Resource Assess Beijing 100037 Peoples R China;
China Univ Geosci Beijing Sch Earth Sci &
Resources 29 Xueyuan Rd Beijing 100083 Peoples R China;
China Univ Geosci Beijing Sch Earth Sci &
Resources 29 Xueyuan Rd Beijing 100083 Peoples R China;
Deep learning; Convolutional neural network; Unsupervised convolutional auto-encoder network; Mineral prospectivity mapping;
机译:矿物前瞻性映射深卷积神经网络的随机滴数据增强
机译:一种新型卷积神经网络与自动编码器的新型融合方法及其在行星齿轮箱故障诊断中的应用
机译:一种多尺度框架,具有无监督的联合培训对扭曲可变形图像配准的卷积神经网络
机译:无监督卷积神经网络与深度信仰网络集成的机械故障诊断的设计与应用
机译:监督和半监控卷积神经网络的鉴定,用于识别中立文本的情感分析
机译:通过基于STDP的无监督预训练和有监督的微调来训练深度尖峰卷积神经网络
机译:基于卷积自动编码器和卷积神经网络的混合模型在面向对象遥感分类中的应用
机译:使用监督无监督人工神经网络的军事应用的流域相似性分析;会议论文