Convolution serves as a foundational component enabling Deep Neural Networks (DNN) to extract meaningful features from input data. This paper focuses on the real-time architecture of a high-throughput ...

Traditional convolution is the foundation of convolutional neural networks (CNNs). It involves sliding a set of learnable filters (kernels) over the input data (e.g., an image) to generate feature ...

Event-based cameras are bio-inspired vision sensors that mimic the sparse and asynchronous activation of the animal retina, offering advantages such as low latency and low computational load in ...

I've seen more and more terms like AI, machine learning, and neural networks, but it's hard to understand what they really are. Therefore, Yulia Gavrilova, a clinical psychologist who also develops ...

1 College of Electronic Science and Technology, National University of Defense Technology, Changsha, China 2 College of Electronics and Internet of Things, Chongqing Polytechnic University of ...

Abstract: Graph-based semi-supervised learning (GSSL) has long been a research focus. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph convolutional ...

Abstract: Graph convolution networks (GCNs) have achieved impressive results for few-shot hyperspectral image (HSI) classification. However, current methods focus on migrating labels from support ...

A website called 'Animated AI' has been published that uses animation to explain 'Convolutional Neural Networks (CNN),' a technology widely used in the field of machine learning. The website visually ...