Earlier quoted context omitted.
How would you describe what a neural ODE is in the simplest possible terms? Let's say I know what an NN and a DE are :).
classic NN takes a vector of data through layers to make a prediction. Backprop adjusts network weights till predictions are right. These network weights form a vector, and training changes this vector till it hits values that mean "trained network". Neural ODE reframes this: instead of focusing on the weights, focus on how they change. It sees training as finding a path from untrained to trained state. At each step,…
Pretty cool approach, looking more into it, thank you!