**PonderNet** is an adaptive computation method that learns to adapt the amount of computation based on the complexity of the problem at hand. PonderNet learns end-to-end the number of computational steps to achieve an effective compromise between training prediction accuracy, computational cost and generalization.

An agreement of a group to follow a common purpose is manifested by its coalescence into a coordinated behavior. The process of initiating this behavior and the period of decision-making by the group members necessarily precedes the coordinated behavior. Given time series of group members’ behavior, the goal is to find these periods of decision-making and identify the initiating individual, if one exists.

Image Source:  [Amornbunchornvej et al.](https://arxiv.org/pdf/1603.01570v2.pdf)

FLICA

Coordination Event Detection and Initiator Identification in Time Series Data

PonderNet

PonderNet: Learning to Ponder

**AlexNet** is a classic convolutional neural network architecture. It consists of convolutions, [max pooling](https://paperswithcode.com/method/max-pooling) and dense layers as the basic building blocks. Grouped convolutions are used in order to fit the model across two GPUs.

Source	PonderNet: Learning to Ponder
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com