Algebraic Positional Encodings
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 preserve...
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creator | Kogkalidis, Konstantinos Bernardy, Jean-Philippe Garg, Vikas |
description | 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. |
doi_str_mv | 10.48550/arxiv.2312.16045 |
format | Article |
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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
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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
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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.</abstract><doi>10.48550/arxiv.2312.16045</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Artificial Intelligence Computer Science - Learning |
title | Algebraic Positional Encodings |
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