Deep neural networks (DNNs) have achieved great success in many applications. The
architectures of DNNs play a crucial role in their performance, which is usually manually …

Abstract Convolutional Neural Network (CNN) is a prevalent topic in deep learning (DL)
research for their architectural advantages. CNN relies heavily on hyperparameter …

As an advanced artificial intelligence technique for solving learning problems, deep learning
(DL) has achieved great success in many real-world applications and attracted increasing …

Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

Deep neural networks have been shown to outperform classical machine learning
algorithms in solving real-world problems. However, the most successful deep neural …

With the emergence of deep neural networks, many research fields, such as image
classification, object detection, speech recognition, natural language processing, machine …

Abstract Squeeze-and-Excitation (SE) blocks have demonstrated significant accuracy gains
for state-of-the-art deep architectures by re-weighting channel-wise feature responses. The …

Deep neural networks (DNNs), which are extensions of artificial neural networks, can learn
higher levels of feature hierarchy established by lower level features by transforming the raw …

Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

Deep learning (DL) has become an important machine learning approach that has been
widely successful in many applications. Currently, DL is one of the best methods of …