Prabhu Teja S

2 accepted papers

2025

CoRet: Improved Retriever for Code Editing

Fabio James Fehr, Prabhu Teja S, Luca Franceschi, Giovanni Zappella

ACL 2025short

In this paper, we introduce CoRet, a dense retrieval model designed for code-editing tasks that integrates code semantics, repository structure, and call-graph dependencies. The model focuses on retrieving relevant portions of a code repository based on natural language queries such as requests to i…

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2021

Uncertainty Reduction for Model Adaptation in Semantic Segmentation

Prabhu Teja S, Francois Fleuret

CVPR 2021poster

Traditional methods for Unsupervised Domain Adaptation (UDA) targeting semantic segmentation exploit information common to the source and target domains, using both labeled source data and unlabeled target data. In this paper, we investigate a setting where the source data is unavailable, but the cl…

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