Neural Interaction

Neural interaction research focuses on understanding and modeling how different entities, whether users in a system, agents in a multi-agent environment, or features in a dataset, interact and influence each other. Current research emphasizes developing models that capture these interactions effectively, often employing neural networks with architectures like bipartite graphs or neural fields, and incorporating constraints to ensure temporal stability or improve interpretability. This work is significant for improving the accuracy and robustness of predictive models across diverse applications, from personalized recommendations and trajectory prediction to industrial process optimization and understanding brain function.