Neural ordinary differential equations describe howvalues change intime.

Tobespecific, our 10 meta-testdatasetsinclude Histology, Drawing, Dessert, Chinese Characters, Speed Limit Signs, Alienvs Predator, Gemstones, and Dog Breeds. Thusweuse Mean Squared Error (MSE) scores.

We introduce a semidefinite programming hierarchy to estimate the global and local Lipschitz constant of a multiple layer deep neural network.

However,theaggregationofdata coming from multiple subjects is challenging, since it requires accounting for largevariability inanatomy,functional topographyandstimulus response across individuals.

One of the most effectivecontinuous deep reinforcement learning algorithms is normalized advantage functions (NAF).