Studying molecular interactions across different data types helps reveal signal transduction mechanisms across different classes of molecules.

Tothebestofour knowledge, our results arethefirst toshowthat reconstructing alargeportion of the actual training samples from a trained neural network classifier is generally possible.

Themodel outputs the normal distribution for the observations, conditional on hidden stateh(t). Since only some features are observed at atime, we mask out the missing values when calculatingLpre. We denote our predicted distribution withppre,and predicted distribution after updating the state with ppost.

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.