In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[EX Q]: In classic papers, Zellner (1988, 2002) demonstrated that Bayesian inference could be derived as the solution to an information theoretic functional.   Below we derive a generalized form of this functional as a variational lower bound of a predictive information bottleneck objective.   This generalized functional encompasses most modern inference procedures and suggests novel ones.
[EX A]: Rederive a wide class of inference procedures from an global information bottleneck objective.

[EX Q]: As distributed approaches to natural language semantics have developed and diversified, embedders for linguistic units larger than words (e.g., sentences) have come to play an increasingly important role.   To date, such embedders have been evaluated using benchmark tasks (e.g., GLUE) and linguistic probes.   We propose a comparative approach, nearest neighbor overlap (N2O), that quantifies similarity between embedders in a task-agnostic manner.   N2O requires only a collection of examples and is simple to understand: two embedders are more similar if, for the same set of inputs, there is greater overlap between the inputs' nearest neighbors.   We use N2O to compare 21 sentence embedders and show the effects of different design choices and architectures.
[EX A]: We propose nearest neighbor overlap, a procedure which quantifies similarity between embedders in a task-agnostic manner, and use it to compare 21 sentence embedders.

[EX Q]: CNNs are widely successful in recognizing human actions in videos, albeit with a great cost of computation. This cost is significantly higher in the case of long-range actions, where a video can span up to a few minutes, on average. The goal of this paper is to reduce the computational cost of these CNNs, without sacrificing their performance. We propose VideoEpitoma,  a neural network architecture comprising two modules: a timestamp selector and a video classifier. Given a long-range video of thousands of timesteps, the selector learns to choose only a few but most representative timesteps for the video. This selector resides on top of a lightweight CNN such as MobileNet and uses a novel gating module to take a binary decision: consider or discard a video timestep. This decision is conditioned on both the timestep-level feature and the video-level consensus. A heavyweight CNN model such as I3D takes the selected frames as input and performs video classification. Using off-the-shelf video classifiers, VideoEpitoma reduces the computation by up to 50\% without compromising the accuracy. In addition, we show that if trained end-to-end, the selector learns to make better choices to the benefit of the classifier, despite the selector and the classifier residing on two different CNNs. Finally, we report state-of-the-art results on two datasets for long-range action recognition: Charades and Breakfast Actions, with much-reduced computation. In particular, we match the accuracy of I3D by using less than half of the computation.


[EX A]:
Efficient video classification using frame-based conditional gating module for selecting most-dominant frames, followed by temporal modeling and classifier.