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Add model SANN #103
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…on due to division by 0
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #103 +/- ##
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- Coverage 85.34% 82.17% -3.18%
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Files 108 112 +4
Lines 3044 3197 +153
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+ Hits 2598 2627 +29
- Misses 446 570 +124 ☔ View full report in Codecov by Sentry. |
…k into martin-sann
…k into martin-sann
…k into martin-sann
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Checklist
Description
This PR adds the SANN [1] model to TBX. It consists of a model that, given predefined computations of neighbourhoods, is probably as expressive as a Simplicial Message Passing Network. It consists mainly of an MLP and aggregations based on Simplicial complexes.
Issue
This is important since it adds a fast and expressive way to test different embedding functions without incurring in costly message passing among simplifies.
Additional context
Refer to Gurugubelli, S., & Chepuri, S. P. (2024). SaNN: Simple Yet Powerful Simplicial-aware Neural Networks. In The Twelfth International Conference on Learning Representations. for more information.