@codewithimanshu@f14bertolotti Pay attention to the pre-print paper that decouples the receptive field from the depth of the model
https://t.co/vJFT18LiIg
Neural Differential Equations treat neural networks as continuous-time systems where hidden states evolve according to differential equations. Instead of discrete layers, they integrate over time, offering memory efficiency and adaptive computation. In deep learning, they’re used for modeling time-series, physical systems, and dynamics in scientific data. Real-world uses include climate modeling, epidemiology, and motion prediction, where continuous evolution is natural.
Image: https://t.co/R1WNcRDnUa