Our proposed formulation for neuron calibration is lightweight and applicable to general feed-forward neural networks-based models.

Asabaseline for DCLM, we conduct extensive experiments and find that model-based filtering is key to assembling a high-quality training set.

In two layer networks, we show how feature learning can dynamically reduce the variance of the final tangent kernel and final network predictions.