Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Geometric Algebra Transformer
Authors: Johann Brehmer, Pim de Haan, Sönke Behrends, Taco S. Cohen
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate GATr in problems from n-body modeling to wall-shear-stress estimation on large arterial meshes to robotic motion planning. GATr consistently outperforms both non-geometric and equivariant baselines in terms of error, data efficiency, and scalability. |
| Researcher Affiliation | Industry | Johann Brehmer Pim de Haan Sönke Behrends Taco Cohen Qualcomm AI Research EMAIL |
| Pseudocode | No | The paper describes the architecture and methods in text and figures (like Fig. 1), but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation of GATr is available at https://github.com/qualcomm-ai-research/geometric-algebra-transformer. |
| Open Datasets | Yes | We use the single-artery wall-shear-stress dataset published by Suk et al. [48]. We train models on the offline trajectory dataset published by Janner et al. [27]. |
| Dataset Splits | Yes | We generate training datasets with n = 4 and between 100 and 105 samples; a validation dataset with n = 4 and 5000 samples; a regular evaluation set with n = 4 and 5000 samples; a number-generalization evaluation set with n = 6 and 5000 samples; and a E(3) generalization set with n = 4, an additional translation (see step 4 above), and 5000 samples. |
| Hardware Specification | No | The paper mentions running experiments on 'GPU' for scaling analysis, but does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper mentions software like 'Py Bullet [13]' and an 'efficient attention implementation by Lefaudeux et al. [32]' (xFormers), but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | All models are trained by minimizing a L2 loss on the final position of all objects. We train for 50 000 steps with the Adam optimizer, using a batch size of 64 and exponentially decaying the learning rate from 3 10 4 to 3 10 6. |