They are categorized in the following sections: Optimization Algorithms; Adversarial Learning, Transfer Learning, and Deep Learning; Signal, Image, and Video Processing; Modeling, Analysis, and Implementation of Neural Networks; Control Systems, Robotics, and Autonomous Driving; Fault Diagnosis and ...
The advent of deep convolutional neural networks (DCNNs) has revolutionized image classification, introducing robust architectures that continue to evolve. However, a notable challenge remains in supervised HSIC due to the scarcity of training samples, a bottleneck that has yet to be comprehensively ...
发放率编码使用时间窗口中脉冲序列的发放率来编码信息,其中实际输入数字被转换为频率与输入值成正比的脉冲序列[Adrian and Zotterman, 1926; Cheng et al., 2020]。时序编码使用单个脉冲的相对时间对信息进行编码,其中输入值通常以精确的时间转换为脉冲序列,包括时间到初次脉冲的编码[VanRullen et al., 2005]、排序...
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Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large...
Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis. Despite the success, the further improvement of dee...
2021. Distributed hybrid CPU and GPU training for graph neural networks on billion-scale graphs. Retrieved from arxiv.org/abs/2112.1534. [194]Zhu Xiaojin and Ghahramani Zoubin. 2002. Learning from labeled and unlabeled data with label propagation. Technical Report....
Domain Agnostic:Learning from Few Samples: A Survey (2020) Neural Networks:Meta-Learning in Neural Networks: A Survey (2020) Domain Agnostic:A Comprehensive Overview and Survey of Recent Advances in Meta-Learning (2020) Domain Agnostic:Baby steps towards few-shot learning with multiple semantics (...
Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but accurate systems using deep neural networks (NN) have only been introduced in the last few...
Before the prevalent of deep learning, color and shape-based features are also used to address traffic sign detection problems [10]. With the rapid advancement of convolutional neural networks (CNNs) in deep learning, some deep learning-based small object detection methods have sprung up. However...