PROGRESSIVE NEURAL ORDINARY DIFFERENTIAL EQUATIONS

Techniques are described for neural networks based on Progressive Neural ODEs (PODEs). In an example, a method to progressively train a neural ordinary differential equation (NODE) model comprises processing, by a machine learning system executed by a computing system, first training data, the first training data having a first complexity, to perform training of a first layer for the NODE model; and after performing the first training, processing second training data, the second training data having a second complexity that is higher than the first complexity, to perform training of a second layer for the NODE model.