D-GRIL: End-to-End Topological Learning with 2-parameter Persistence
Soham Mukherjee, Shreyas N. Samaga, Cheng Xin, Steve Oudot, Tamal K. Dey
6/11/2024
cs.LGcs.AImath.AT
No official code found. Best implementation: TDA-Jyamiti/d-gril (2 stars, MIT, last commit 2024-08-11).
Abstract
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.
