**Parrot** is an imitation learning approach to automatically learn cache access patterns by leveraging Belady’s optimal policy. Belady’s optimal policy is an oracle policy that computes the theoretically optimal cache eviction decision based on knowledge of future cache accesses, which Parrot approximates with a policy that only conditions on the past accesses.

**JLA**, or **Joint Learning Architecture**, is an approach for multiple object tracking and trajectory forecasting. It jointly trains a tracking and trajectory forecasting model, and the trajectory forecasts are used for short-term motion estimates in lieu of linear motion prediction methods such as the Kalman filter. It uses a [FairMOT](https://paperswithcode.com/method/fairmot) model as the base model because this architecture already performs detection and tracking. A forecasting branch is added to the network and is trained end-to-end. [FairMOT](https://paperswithcode.com/method/fairmot) consist of a backbone network utilizing [Deep Layer Aggregation](https://www.paperswithcode.com/method/dla), an object detection head, and a reID head.

Joint Learning Architecture for Multiple Object Tracking and Trajectory Forecasting

Parrot

An Imitation Learning Approach for Cache Replacement

**Child-Tuning** is a fine-tuning technique that updates a subset of parameters (called child network) of large pretrained models via strategically masking out the gradients of the non-child network during the backward process. It decreases the hypothesis space of the model via a task-specific mask applied to the full gradients, helping to effectively adapt the large-scale pretrained model to various tasks and meanwhile aiming to maintain its original generalization ability.

Source	An Imitation Learning Approach for Cache Replacement
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com