1. A method comprising: extracting time-dependent features from an input, to yield extracted time-dependent features; selecting a plurality of time-dependent features from the extracted time-dependent features using a plurality of selection strategies, wherein a plurality of pooling interface units select the plurality of time-dependent features based on a weighted average score and on a rectified average score of the extracted time-dependent features; generating a plurality of feature vectors by pooling the plurality of time-dependent features using a plurality of pooling interface units; generating a plurality of scores associated with the plurality of feature vectors; and returning, in response to the input, a class label selected based on the plurality of scores.