UniGenCoder: Merging Seq2Seq and Seq2Tree Paradigms for Unified Code Generation
Deep learning-based code generation has completely transformed the way developers write programs today. Existing approaches to code generation have focused either on the Sequence-to-Sequence paradigm, which generates target code as a sequence of tokens, or the Sequence-to-Tree paradigm, which output...
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| Main Authors: | , , , |
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| Format: | Conference Proceeding |
| Language: | English |
| Subjects: |
Computing methodologies
> Machine learning
> Learning paradigms
> Supervised learning
> Supervised learning by regression
Computing methodologies
> Machine learning
> Learning paradigms
> Unsupervised learning
> Dimensionality reduction and manifold learning
Computing methodologies
> Machine learning
> Machine learning approaches
> Bio-inspired approaches
> Genetic programming
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| Online Access: | Request full text |
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