Abstract: Systems and methods for training and utilizing predictive models that ignore missing features in accordance with embodiments of the invention are illustrated. One embodiment includes a method for generating representations of inputs with missing values. The method includes steps for, at a single layer in a multi-layer model, receiving an input includes a set of one or more values for several features and identifying a missingness pattern of the input, wherein the missingness pattern indicates whether the set of values is missing a value for each of the several features. The method further includes determining a set of one or more transformation weights based on the missingness pattern and transforming the input based on the determined transformation weights.

Abstract: One embodiment includes a method for generating representations of inputs with missing values. The method includes steps for receiving an input includes a set of one or more values for several features, wherein the set of values for at least one of the several features includes values for each of several points in time, and for identifying a missingness pattern of the input, wherein the missingness pattern for the at least one feature indicates whether the set of values is missing a value for each of the several points in time. The method further includes steps for determining a set of one or more transformation weights based on the missingness pattern, and transforming the input based on the determined transformation weights.