D-GRIL: End-to-End Topological Learning with 2-parameter Persistence
<|reference_start|>D-GRIL: End-to-End Topological Learning with 2-parameter Persistence: 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.<|reference_end|>
@article{mukherjee2024d-gril:,
    title={D-GRIL: End-to-End Topological Learning with 2-parameter Persistence},
    author={Soham Mukherjee, Shreyas N. Samaga, Cheng Xin, Steve Oudot, Tamal K.
  Dey},
    journal={arXiv preprint arXiv:2406.07100},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07100},
    primaryClass={cs.LG cs.AI math.AT}
}