Leveraging artificial intelligence and machine learning to address complex scientific challenges across diverse domains, including civil engineering, chemistry, fluid dynamics, plant biology, and materials science.
Apr 30, 2022
This project integrates two recent advances in differentiable DAG learning: (1) analytic DAG constraints for improved computational stability and (2) Truncated Matrix Power Iteration (TMPI) for escaping gradient vanishing. The methods are applied to causal discovery and structure learning in high-dimensional settings.
Oct 1, 2021