Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural ...

(AI). It involves training artificial neural networks to learn and make decisions from large amounts of data. Deep learning algorithms are modeled after the structure and function of the human brain, ...

Section 3 introduces the fundamental principles of spiking neural networks. Section 4 focuses on the most recent advanced SNN models and architectures, especially transformer-based SNNs. Section 5 ...

there is a rich variety of NLP problems that can be best expressed with a graph structure. As a result, there is a surge of interest in developing new deep learning techniques on graphs for a large ...

A new study proposes NdLinear, a multi-dimensional linear layer that preserves data structure and slashes parameter counts ...

Researchers have developed a new kind of artificial neuron—called infomorphic neurons—that can independently learn and ...

Abstract: Understanding the effect of hyperparameters of the network structure on the performance of Convolutional Neural Networks (CNNs) remains the most fundamental and urgent issue in deep learning ...

Researchers from the Yunnan Observatories of the Chinese Academy of Sciences and Southwest Forestry University have developed an advanced neural network ... implemented a deep learning approach ...

Department of Mechanical Engineering, Chungbuk National University (CBNU), 1, Chungdae-ro, Seowon-gu, Cheongju-si, Chungcheongbuk-do 28644, Republic of Korea ...