  We introduce modifications to state-of-the-art approaches to aggregating
local video descriptors by using attention mechanisms and function
approximations. Rather than using ensembles of existing architectures, we
provide an insight on creating new architectures. We demonstrate our solutions
in the "The 2nd YouTube-8M Video Understanding Challenge", by using frame-level
video and audio descriptors. We obtain testing accuracy similar to the state of
the art, while meeting budget constraints, and touch upon strategies to improve
the state of the art. Model implementations are available in
https://github.com/pomonam/LearnablePoolingMethods.
