Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918506882138112 |
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| author | Shen, Zhaiming Havrilla, Alex Lai, Rongjie Cloninger, Alexander Liao, Wenjing |
| author_facet | Shen, Zhaiming Havrilla, Alex Lai, Rongjie Cloninger, Alexander Liao, Wenjing |
| contents | Transformers serve as the foundational architecture for large language and video generation models, such as GPT, BERT, SORA and their successors. Empirical studies have demonstrated that real-world data and learning tasks exhibit low-dimensional structures, along with some noise or measurement error. The performance of transformers tends to depend on the intrinsic dimension of the data/tasks, though theoretical understandings remain largely unexplored for transformers. This work establishes a theoretical foundation by analyzing the performance of transformers for regression tasks involving noisy input data near a manifold. Specifically, the input data are in a tubular neighborhood of a manifold, while the ground truth function depends on the projection of the noisy data onto this manifold, referred to as the task-level manifold. We prove approximation and generalization errors which crucially depend on the intrinsic dimension of the task-level manifold. Our results demonstrate that transformers can leverage low-complexity structures in learning task even when the input data are perturbed by high-dimensional noise. Our novel proof technique constructs representations of basic arithmetic operations by transformers, which may hold independent interest. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03205 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights Shen, Zhaiming Havrilla, Alex Lai, Rongjie Cloninger, Alexander Liao, Wenjing Machine Learning Numerical Analysis Statistics Theory Transformers serve as the foundational architecture for large language and video generation models, such as GPT, BERT, SORA and their successors. Empirical studies have demonstrated that real-world data and learning tasks exhibit low-dimensional structures, along with some noise or measurement error. The performance of transformers tends to depend on the intrinsic dimension of the data/tasks, though theoretical understandings remain largely unexplored for transformers. This work establishes a theoretical foundation by analyzing the performance of transformers for regression tasks involving noisy input data near a manifold. Specifically, the input data are in a tubular neighborhood of a manifold, while the ground truth function depends on the projection of the noisy data onto this manifold, referred to as the task-level manifold. We prove approximation and generalization errors which crucially depend on the intrinsic dimension of the task-level manifold. Our results demonstrate that transformers can leverage low-complexity structures in learning task even when the input data are perturbed by high-dimensional noise. Our novel proof technique constructs representations of basic arithmetic operations by transformers, which may hold independent interest. |
| title | Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights |
| topic | Machine Learning Numerical Analysis Statistics Theory |
| url | https://arxiv.org/abs/2505.03205 |