Decoupled Progressive Distillation for Sequential Prediction with Interaction Dynamics.

The article focuses on proposing a sequential prediction framework with Decoupled Progressive Distillation (DePoD), addressing challenges in capturing comprehensive intents for resource allocation in sequential prediction. Topics include sequential prediction, representation learning, and interaction dynamics in modeling intents for improving accuracy-based metrics in various applications such as recommender systems and predictive policing.