Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.07405 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911317524217856 |
|---|---|
| author | Ji, Zhongping |
| author_facet | Ji, Zhongping |
| contents | This research endeavors to offer insights into unlocking the further potential of transformer-based architectures. One of the primary motivations is to offer a geometric interpretation for the attention mechanism in transformers. In our framework, the attention mainly involves metric tensors, tangent spaces, inner product, and how they relate to each other. These quantities and structures at discrete positions are intricately interconnected via the parallel transport of tangent vectors. To make the learning process more efficient, we reduce the number of parameters through ingenious predefined configurations. Moreover, we introduce an explicit mechanism to highlight a neighborhood by attenuating the remote values, given that transformers inherently neglect local inductive bias. Experimental results demonstrate that our modules deliver significant performance improvements relative to the baseline. More evaluation experiments on visual and large language models will be launched successively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07405 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RiemannFormer: A Framework for Attention in Curved Spaces Ji, Zhongping Machine Learning This research endeavors to offer insights into unlocking the further potential of transformer-based architectures. One of the primary motivations is to offer a geometric interpretation for the attention mechanism in transformers. In our framework, the attention mainly involves metric tensors, tangent spaces, inner product, and how they relate to each other. These quantities and structures at discrete positions are intricately interconnected via the parallel transport of tangent vectors. To make the learning process more efficient, we reduce the number of parameters through ingenious predefined configurations. Moreover, we introduce an explicit mechanism to highlight a neighborhood by attenuating the remote values, given that transformers inherently neglect local inductive bias. Experimental results demonstrate that our modules deliver significant performance improvements relative to the baseline. More evaluation experiments on visual and large language models will be launched successively. |
| title | RiemannFormer: A Framework for Attention in Curved Spaces |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.07405 |