Sustainability advocate and AI engineer Sathya Kannan has recently unveiled a framework that claims to be capable of reducing global carbon dioxide (CO₂) emissions leveraging AI neural networks. In ...
Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...
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 ...
Software engineers developing artificial intelligence (AI) models using standard frameworks such as Keras, PyTorch, and TensorFlow are usually not well-equipped to translate those models into ...
Lightweight convolutional neural networks improved lung cancer classification accuracy in histopathological images while ...
Smooth developer experience is fundamental in artificial intelligence designs. Development toolkits can streamline the preparation of trained neural networks for edge and low-latency data-center ...
BEIJING - WiMi Hologram Cloud Inc. (NASDAQ:WIMI) has developed a Lean Classical-Quantum Hybrid Neural Network (LCQHNN) framework aimed at improving image classification efficiency while minimizing ...
Neural phase retrieval (NeuPh) employs a CNN-based encoder to learn measurement-specific information and encode them into a latent-space representation. The MLP decoder reconstructs the phase values ...
For example, a Convolutional Neural Network (CNN) trained on thousands of radar echoes can recognize the unique spatial signature of a small metallic fragment, even when its signal is partially masked ...
Artificial intelligence (AI) is increasingly transforming computational mechanics, yet many AI-driven models remain limited by poor interpretability, weak generalization, and insufficient physical ...
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