Activity Prediction

Activity prediction focuses on forecasting future events or behaviors based on observed data, aiming to improve the accuracy and efficiency of various applications. Current research emphasizes developing robust models, including those based on graph neural networks, Bayesian nonparametric approaches, and large language models integrated with geometric reasoning, to handle diverse data types and complexities like imbalanced datasets and long-horizon predictions. These advancements are crucial for improving decision-making in fields ranging from healthcare (predicting patient needs) and robotics (anticipating human actions for safe interaction) to drug discovery (predicting molecular activity and identifying activity cliffs). The development of large-scale benchmark datasets and improved model interpretability are also key areas of focus.