Deep learning is a subset of machine learning that uses multi-layer neural networks to find patterns in complex, unstructured data like images, text, and audio. What sets deep learning apart is its ...

Earlier this year, he testified before the U.S. Senate on the problems with ed tech; his testimony has already been viewed more than 2 million times. I wanted to hear more about his provocative ...

Mingai Li, received her B.Sc. degree and M.Sc. degree from Daqing Petroleum Institute, Heilongjiang, China, in 1987 and 1990 respectively, and Ph.D. degree from Beijing University of Technology, ...

Abstract: Leveraging the power of deep reinforcement learning (DRL) and strategic knowledge transfer, our study introduces PIRA-DRL-DTRL, a novel approach to optimizing resource allocation in emerging ...

Accurate prediction of joint torque is critical for preventing injury by providing precise insights into the forces acting on joints during activities. Traditional approaches, including inverse ...

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 ...

In 2024, college sports are as different as they’ve ever been. Players routinely bounce around the nation looking for the best place to maximize their talent and get an opportunity to make it to the ...

Abstract: Dear Editor, This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning (DMARL) to reduce the convergence difficulty and training time when ...

Between January 2004 and June 2021, 2,677 patients with 12,255 CT reports and 670 patients with 3,058 CT reports were allocated to training and internal testing data sets, respectively. ClinicalBERT ...