Convolutional neural networks (CNN) are highly effective for image classification and
computer vision activities. The accuracy of CNN architecture depends on the design and …

Convolutional Neural Networks (CNNs) models achieve a dominant performance on
immense domains. There are CNNs that come in numerous topologies of different sizes …

Deep learning (DL) has made remarkable progress on various real-world tasks, but its
construction pipeline strongly relies on human scientists. Furthermore, evolutionary …

In the last decade, there has been continuously increasing attention on Neural Architecture
Search (NAS). The design of network architecture is aimed at automatically generating …

Deep learning models for computer vision applications specifically and for machine learning
generally are now the state of the art. The growth of size and complexity of neural networks …

神经网络优化是机器学习领域的一个基础性前沿课题. 相较于神经网络的纯梯度优化算法,
非梯度算法在解决收敛速度慢, 易陷入局部最优, 无法解决不可微等问题上表现出更大的优势 …

Deep learning has improved the state-of-the-art in sentiment analysis for various languages,
including Arabic. One aspect that can affect the performance of deep learning-based …

Abstract The Convolutional Neural Network (CNN) is a complex architecture that performs
magnificently in image classification and segmentation problems. Still, selecting an effective …

The analysis of chromosomes is a significant and challenging task for clinical diagnosis and
biological research. The technique based on color imaging is a multiplex fluorescent in situ …

As an epitome of deep learning, convolutional neural network (CNN) has shown its
advantages in solving many real-world problems. Successful CNN applications on medical …