Lecturer, The University of Tokyo
2 papers at NeurIPS 2025
Inspired by Ebbinghaus' forgetting curve, we propose a continuous state space model for dynamic graph modeling and achieve state-of-the-art performance.
We propose a principled framework for Continuous Domain Generalization that models the geometric and algebraic structure of parameter evolution, enabling robust generalization across domains with continuous variation.
