End-to-end topological learning using 1-parameter persistence is well-known. We show that the framework can be enhanced using 2-parameter persistence by adopting a recently introduced 2-parameter persistence based vectorization technique called GRIL. We establish a theoretical foundation of differentiating GRIL producing D-GRIL. We show that D-GRIL can be used to learn a bifiltration function on standard benchmark graph datasets. Further, we exhibit that this framework can be applied in the context of bio-activity prediction in drug discovery.
View on arXiv@article{mukherjee2025_2406.07100,
title={ D-GRIL: End-to-End Topological Learning with 2-parameter Persistence },
author={ Soham Mukherjee and Shreyas N. Samaga and Cheng Xin and Steve Oudot and Tamal K. Dey },
journal={arXiv preprint arXiv:2406.07100},
year={ 2025 }
}