Progressive neural ordinary differential equations

A method to progressively train a neural ordinary differential equation (NODE) model, the method comprising:

• processing, by a machine learning system executed by a computing system, first training data, the first training data comprising irregularly spaced time series data derived from second training data by applying a complexity-reducing transformation that reduces frequency content of the second training data, to perform training of a first layer for the NODE model, the first layer comprising a first ordinary differential equation (ODE)-based transfer function parameterized by a first set of parameters;
• and
• after processing the first training data to perform the training of the first layer for the NODE model, processing, while retaining the first set of parameters of the first layer fixed, the second training data comprising irregularly spaced time series data to perform training of a second layer for the NODE model, the second layer different from the first layer and comprising a second ODE-based transfer function parameterized by a second set of parameters.

Non-transitory computer readable media comprising instructions for causing processing circuitry to execute a machine learning system to progressively train a neural ordinary differential equation (NODE) model, wherein the machine learning system is configured to:

• process first training data, the first training data comprising irregularly spaced time series data derived from second training data by applying a complexity-reducing transformation that reduces frequency content of the second training data, to perform training of a first layer for the NODE model, the first layer comprising a first ordinary differential equation (ODE)-based transfer function parameterized by a first set of parameters;
• and
• after processing the first training data to perform the training of the first layer for the NODE model, process, while retaining the first set of parameters of the first layer fixed, the second training data comprising irregularly spaced time series data to perform training of a second layer for the NODE model, the second layer different from the first layer and comprising a second ODE-based transfer function parameterized by a second set of parameters.