on a small and local neighborhood.

We aim to find an attention cell from the proposed search space such that the video classification accuracy is maximized when adding that attention cell into the backbone network. But the search process can be extremely costly. One significant bottleneck of the search is the need to constantly evaluate different attention cells. Evaluating the performance of an attention cell typically requires training the selected attention cell as well as the backbone network from scratch, which can take days on large-scale video datasets, e.g., Kinetics-600 [@carreira2018short].

To alleviate this bottleneck, we consider two search algorithms: (1) Gaussian Process Bandit (GPB) [@Srinivas2009GaussianPO; @snoek2012practical], which judiciously selects the next attention cell for evaluation based on the attention cells having been evaluated so far, allowing us to find high-performing attention cells within a limited number of trials; (2) differentiable architecture search [@liu2018darts], where we develop a differentiable formulation of the proposed search space, making it possible to jointly learn the attention cell design and network weights through back-propagation, without explicitly sampling and evaluating different cells. The entire differentiable search process only consumes a computational cost similar to fully training one network on the training videos. This formulation also 