Learning Structure-Aware Representations of Dependent Types
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914997312946176 |
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| author | Kogkalidis, Konstantinos Melkonian, Orestis Bernardy, Jean-Philippe |
| author_facet | Kogkalidis, Konstantinos Melkonian, Orestis Bernardy, Jean-Philippe |
| contents | Agda is a dependently-typed programming language and a proof assistant, pivotal in proof formalization and programming language theory. This paper extends the Agda ecosystem into machine learning territory, and, vice versa, makes Agda-related resources available to machine learning practitioners. We introduce and release a novel dataset of Agda program-proofs that is elaborate and extensive enough to support various machine learning applications -- the first of its kind. Leveraging the dataset's ultra-high resolution, which details proof states at the sub-type level, we propose a novel neural architecture targeted at faithfully representing dependently-typed programs on the basis of structural rather than nominal principles. We instantiate and evaluate our architecture in a premise selection setup, where it achieves promising initial results, surpassing strong baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02104 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Structure-Aware Representations of Dependent Types Kogkalidis, Konstantinos Melkonian, Orestis Bernardy, Jean-Philippe Machine Learning Programming Languages Agda is a dependently-typed programming language and a proof assistant, pivotal in proof formalization and programming language theory. This paper extends the Agda ecosystem into machine learning territory, and, vice versa, makes Agda-related resources available to machine learning practitioners. We introduce and release a novel dataset of Agda program-proofs that is elaborate and extensive enough to support various machine learning applications -- the first of its kind. Leveraging the dataset's ultra-high resolution, which details proof states at the sub-type level, we propose a novel neural architecture targeted at faithfully representing dependently-typed programs on the basis of structural rather than nominal principles. We instantiate and evaluate our architecture in a premise selection setup, where it achieves promising initial results, surpassing strong baselines. |
| title | Learning Structure-Aware Representations of Dependent Types |
| topic | Machine Learning Programming Languages |
| url | https://arxiv.org/abs/2402.02104 |