Algebraic Positional Encodings
Fuente:
arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866912097535787008 |
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| author | Kogkalidis, Konstantinos Bernardy, Jean-Philippe Garg, Vikas |
| author_facet | Kogkalidis, Konstantinos Bernardy, Jean-Philippe Garg, Vikas |
| contents | We introduce a novel positional encoding strategy for Transformer-style models, addressing the shortcomings of existing, often ad hoc, approaches. Our framework provides a flexible mapping from the algebraic specification of a domain to an interpretation as orthogonal operators. This design preserves the algebraic characteristics of the source domain, ensuring that the model upholds its desired structural properties. Our scheme can accommodate various structures, ncluding sequences, grids and trees, as well as their compositions. We conduct a series of experiments to demonstrate the practical applicability of our approach. Results suggest performance on par with or surpassing the current state-of-the-art, without hyper-parameter optimizations or "task search" of any kind. Code is available at https://github.com/konstantinosKokos/ape. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_16045 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Algebraic Positional Encodings Kogkalidis, Konstantinos Bernardy, Jean-Philippe Garg, Vikas Machine Learning Artificial Intelligence We introduce a novel positional encoding strategy for Transformer-style models, addressing the shortcomings of existing, often ad hoc, approaches. Our framework provides a flexible mapping from the algebraic specification of a domain to an interpretation as orthogonal operators. This design preserves the algebraic characteristics of the source domain, ensuring that the model upholds its desired structural properties. Our scheme can accommodate various structures, ncluding sequences, grids and trees, as well as their compositions. We conduct a series of experiments to demonstrate the practical applicability of our approach. Results suggest performance on par with or surpassing the current state-of-the-art, without hyper-parameter optimizations or "task search" of any kind. Code is available at https://github.com/konstantinosKokos/ape. |
| title | Algebraic Positional Encodings |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2312.16045 |