GRaM 2026
GRaM Workshop @ ICLR 2026 — Blogpost Track
The GRaM workshop aims to provide a platform that fosters learning, collaboration, and the advancement of the geometry-grounded methods in machine learning. In this Blogpost track, we intend to encourage transparent discussions and opinions in the field, and make geometric machine learning more accessible.
This blogpost track is directly inspired by the amazing ICLR blogpost track.
Accepted Posts
| Title | Author(s) |
|---|---|
| Jacobi Fields in Machine Learning | Olga Zaghen |
| Fewer Edges, Faster Protein Graph Learning | Pau Hidalgo-Pujol, Manel Gil-Sorribes, Alexis Molina, Bertran Miquel-Oliver |
| When the k-NN Metric Breaks | Francesco Orsi |
| Crystalite: A Lightweight Transformer for Crystal Modeling | Tin Hadzi Veljkovic |
| To Augment or Not to Augment? | Hannah Lawrence, Elyssa Hofgard, Vasco Portilheiro, Yuxuan Chen, Tess Smidt, Robin Walters |
| 4-Dimensional Objects as a Tool to Study Symmetry Learning | S M Raihan Gafur |
| Blowup and Blowdown in Deep Learning | Hikaru Matsuoka |
| TOPOS: Topological Optimal-transport Partitioned Operator Solver | Mamta Saini |
| Graph Mamba - Rethinking Graph Learning | Vladislav Kalinichenko, Polina Korobeinikova |
| Symmetry Increase and Equivariant Feature Selection | Ning Lin, Jiacheng Cen, Anyi Li, Wenbing Huang, Hao Sun |
| The Role of Directionality in Graph Neural Networks | Bertran Miquel-Oliver, Manel Gil-Sorribes, Alexis Molina |