FAIR: Flow Type-Aware Pre-Training of Compiler Intermediate Representations-Reference-Cited by-同舟云学术

FAIR: Flow Type-Aware Pre-Training of Compiler Intermediate Representations

Published:2024-02-06 Issue: Volume:34 Page:1-12
ISSN:
Container-title:Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
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Author:

Niu Changan¹^ORCID,Li Chuanyi¹^ORCID,Ng Vincent²^ORCID,Lo David³^ORCID,Luo Bin¹^ORCID

Affiliation:

1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China

2. Human Language Technology Research Institute, University of Texas at Dallas, Richardson, TX 75083-0688, Dallas, Texas, United States of America

3. School of Computing and Information Systems, Singapore Management University, Singapore, Singapore

Funder

Cooperation Fund of Huawei-NJU Creative Laboratory for the Next Programming

CCF-Huawei Populus Grove Fund

NSF award

National Research Foundation Singapore

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3597503.3608136

Reference61 articles.

1. Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, and Hanie Sedghi. 2021. Exploring the limits of large scale pre-training. arXiv preprint arXiv:2110.02095 (2021).

2. Unified Pre-training for Program Understanding and Generation

3. Uri Alon and Eran Yahav. 2021. On the Bottleneck of Graph Neural Networks and its Practical Implications. In International Conference on Learning Representations. https://openreview.net/forum?id=i80OPhOCVH2

4. Jimmy Ba and Rich Caruana. 2014. Do deep nets really need to be deep? Advances in neural information processing systems 27 (2014).

5. Tal Ben-Nun, Alice Shoshana Jakobovits, and Torsten Hoefler. 2018. Neural code comprehension: A learnable representation of code semantics. Advances in Neural Information Processing Systems 31 (2018).

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