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 ...

Bridging speed and accuracy in radiation therapy QA Led by Professor Fu Jin, the study addresses a critical challenge in ...

Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting expertise.From neural networks to N ...

During my first semester as a computer science graduate student at Princeton, I took COS 402: Artificial Intelligence. Toward the end of the semester, there was a lecture about neural networks. This ...

Accurate segmentation of medical images is essential for clinical decision-making, and deep learning techniques have shown remarkable results in this area. However, existing segmentation models that ...

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...

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 ...

A new artificial intelligence approach combines deep learning with physical modeling to extract detailed aerosol properties from complex satellite observations. By learning how light intensity and ...

Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known ...