3 accepted papers
Neural Differential Equations (NDEs) excel at modeling continuous-time dynamics, effectively handling challenges such as irregular observations, missing values, and noise. Despite their advantages, NDEs face a fundamental challenge in adopting dropout, a cornerstone of deep learning regularization,
Time series modeling and analysis have become critical in various domains. Conventional methods such as RNNs and Transformers, while effective for discrete-time and regularly sampled data, face significant challenges in capturing the continuous dynamics and irregular sampling patterns inherent in re
This paper proposes a MIMO imaging method with an iterative-based super-resolution technique for automotive radar applications. Vehicle radars have recently used 4D imaging radar, offering improved detection ranges and high-resolution capability. The application of imaging radar technology aims to e…