A team at the University of California, San Diego has redesigned how RRAM operates in an effort to accelerate the execution ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

It’s been ten years since AlexNet, a deep learning convolutional neural network (CNN) model running on GPUs, displaced more traditional vision processing algorithms to win the ImageNet Large Scale ...

Earth Scientists have used machine learning for at least three decades and the applications span is large, from remote sensing to analysis of well log data, among many others. Although machine ...

Recent advances in neural network methodologies have significantly reshaped the fields of electrical tomography and moisture analysis. By integrating artificial neural networks (ANNs) for both image ...

Lightweight convolutional neural networks improved lung cancer classification accuracy in histopathological images while ...

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...

Researchers have developed an easy-to-use optical chip that can configure itself to achieve various functions. The positive real-valued matrix computation they have achieved gives the chip the ...

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...

This technical paper titled “Can one hear the shape of a neural network?: Snooping the GPU via Magnetic Side Channel” was presented by researchers at Columbia University, Adobe Research and University ...