3 papers
cs.LG2026
Physical Self-Supervised Learning: IMU Sensing without Manual Labels
Yuyang Leng, Renyuan Liu, Shaohan Hu +4
Deep neural networks have become a promising approach for IMU-based sensing, but their scalability is fundamentally limited by costly labeled data and poor robustness to heterogene…
physics.bio-ph2025
Error Bound Analysis of Physics-Informed Neural Networks-Driven T2 Quantification in Cardiac Magnetic Resonance Imaging
Mengxue Zhang, Qingrui Cai, Yinyin Chen +13
Physics-Informed Neural Networks (PINN) are emerging as a promising approach for quantitative parameter estimation of Magnetic Resonance Imaging (MRI). While existing deep learning…
cs.NI2025
DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training
Renyuan Liu, Yuyang Leng, Kaiyan Liu +6
Recent advancements in on-device training for deep neural networks have underscored the critical need for efficient activation compression to overcome the memory constraints of mob…