Continuous Depth

Continuous-depth neural networks represent a paradigm shift in deep learning, moving away from discrete layers to models defined by differential equations, thereby enabling continuous evolution of network states over time and depth. Current research focuses on various architectures, including Neural Ordinary Differential Equations (NODEs) and their extensions like Neural Delay Differential Equations (NDDEs) and continuous-depth recurrent networks, aiming to improve model expressiveness, generalization, and efficiency. These advancements offer enhanced capabilities for modeling complex dynamical systems and time-series data, with implications for diverse applications ranging from image classification to system identification and forecasting. The exploration of sparsity and efficient training methods within these continuous-depth frameworks is a key area of ongoing investigation.