Quality inspection and defect detection remain critical challenges across diverse industrial
applications. Driven by advancements in Deep Learning, Convolutional Neural Networks …

摘要作为一个十余年来快速发展的崭新领域, 深度学**受到了越来越多研究者的关注,
它在特征提取和模型拟合上都有着相较于浅层模型显然的优势. 深度学**善于从原始输入数据中 …

Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing
industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of …

Deep learning-based machine learning models have shown significant results in speech
recognition and numerous vision-related tasks. The performance of the present speech-to …

Convolutional Neural Networks (CNN) are very computation-intensive. Recently, a lot of
CNN accelerators based on the CNN intrinsic parallelism are proposed. However, we …

We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on
single-channel electroencephalography (EEG) to learn task-specific filters for classification …

The machine recognition of speech spoken at a distance from the microphones, known as
far-field automatic speech recognition (ASR), has received a significant increase in attention …

CNN is inspired from Primary Visual (V1) neurons. It is a typical deep learning technique
and can help teach machine how to see and identify objects. In the most recent decade …

Crime prediction is of great significance to the formulation of policing strategies and the
implementation of crime prevention and control. Machine learning is the current mainstream …

Long short-term memory (LSTM) recurrent neural networks (RNNs) have recently shown
significant performance improvements over deep feed-forward neural networks (DNNs). A …