Hardware fragmentation remains a persistent bottleneck for deep learning engineers seeking consistent performance.

The efficacy of deep residual networks is fundamentally predicated on the identity shortcut connection. While this mechanism effectively mitigates the vanishing gradient problem, it imposes a strictly ...

Beijing, Jan. 05, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning BEIJING, Jan. 05, 2026––WiMi Hologram ...

SHENZHEN, China, Dec. 18, 2025 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, innovatively launches a quantum-enhanced deep ...

A research team led by Chang Keke from the Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy of Sciences (CAS), has developed an innovative machine learning framework ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

Abstract: This paper proposes a novel graph convolutional deep clustering approach for radar signal deinterleaving. The algorithm features an innovative PRI Frequency-Driven Adjacency Matrix ...

1 State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, China 2 College of Geophysics, Chengdu University of Technology, Chengdu, China ...

ABSTRACT: Rainfall-induced landslides threaten mountainous regions globally, yet existing models face challenges in real-time, large-scale prediction due to dependency on post-event data. This study ...

NEET UG 2025 Counselling: The counselling process for vacant seats of MBBS/ BDS/ BSc (NURSING) in All India Quota (AIQ) seats/ Deemed University/ Central University by the Medical Council Committee ...

The early and accurate diagnosis of Alzheimer's Disease and Frontotemporal Dementia remains a critical challenge, particularly with traditional machine learning models which often fail to provide ...