D-GRIL: End-to-End Topological Learning with 2-parameter Persistence
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909504817332224 |
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| author | Mukherjee, Soham Samaga, Shreyas N. Xin, Cheng Oudot, Steve Dey, Tamal K. |
| author_facet | Mukherjee, Soham Samaga, Shreyas N. Xin, Cheng Oudot, Steve Dey, Tamal K. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07100 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | D-GRIL: End-to-End Topological Learning with 2-parameter Persistence Mukherjee, Soham Samaga, Shreyas N. Xin, Cheng Oudot, Steve Dey, Tamal K. Machine Learning Artificial Intelligence Algebraic Topology 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. |
| title | D-GRIL: End-to-End Topological Learning with 2-parameter Persistence |
| topic | Machine Learning Artificial Intelligence Algebraic Topology |
| url | https://arxiv.org/abs/2406.07100 |