Summary of D-gril: End-to-end Topological Learning with 2-parameter Persistence, by Soham Mukherjee et al.
D-GRIL: End-to-End Topological Learning with 2-parameter Persistence
by Soham Mukherjee, Shreyas N. Samaga, Cheng Xin, Steve Oudot, Tamal K. Dey
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Algebraic Topology (math.AT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method enhances end-to-end topological learning using 1-parameter persistence by adopting a recently introduced vectorization technique called GRIL, based on 2-parameter persistence. Theoretical foundations are established for differentiating GRIL to produce D-GRIL, which can be used to learn bifiltration functions on standard graph datasets. This framework is applied in bio-activity prediction in drug discovery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand complex data patterns is introduced by combining two techniques: 1-parameter persistence and 2-parameter persistence. This combination helps computers better recognize patterns in biological data, which can lead to improved predictions for finding effective treatments for diseases. |
Keywords
» Artificial intelligence » Vectorization
