Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines
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[1] Jing Yu Koh,et al. Scaling Autoregressive Models for Content-Rich Text-to-Image Generation , 2022, Trans. Mach. Learn. Res..
[2] Xin Wang,et al. CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training , 2022, NAACL-HLT.
[3] Cherepanov,et al. Competition-level code generation with AlphaCode , 2022, Science.
[4] Alexandre Muzio,et al. Scalable and Efficient MoE Training for Multitask Multilingual Models , 2021, ArXiv.
[5] Yue Wang,et al. CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation , 2021, EMNLP.
[6] Katikapalli Subramanyam Kalyan,et al. AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing , 2021, ArXiv.
[7] Li Li,et al. SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation , 2021, 2108.04556.
[8] Iqbal H. Sarker. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions , 2021, SN Computer Science.
[9] Wojciech Zaremba,et al. Evaluating Large Language Models Trained on Code , 2021, ArXiv.
[10] Nan Duan,et al. CoSQA: 20,000+ Web Queries for Code Search and Question Answering , 2021, ACL.
[11] Lei Lyu,et al. TreeBERT: A Tree-Based Pre-Trained Model for Programming Language , 2021, UAI.
[12] Veronika Thost,et al. CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks , 2021, NeurIPS Datasets and Benchmarks.
[13] Hieu Tran,et al. CoTexT: Multi-task Learning with Code-Text Transformer , 2021, NLP4PROG.
[14] Danijel Skocaj,et al. Mixed supervision for surface-defect detection: from weakly to fully supervised learning , 2021, Comput. Ind..
[15] M. V. Koroteev. BERT: A Review of Applications in Natural Language Processing and Understanding , 2021, ArXiv.
[16] Kai-Wei Chang,et al. Unified Pre-training for Program Understanding and Generation , 2021, NAACL.
[17] Neel Sundaresan,et al. CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation , 2021, NeurIPS Datasets and Benchmarks.
[18] S. Gelly,et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , 2020, ICLR.
[19] Ming Zhou,et al. GraphCodeBERT: Pre-training Code Representations with Data Flow , 2020, ICLR.
[20] Martin Maas,et al. A Taxonomy of ML for Systems Problems , 2020, IEEE Micro.
[21] Joseph E. Gonzalez,et al. Contrastive Code Representation Learning , 2020, EMNLP.
[22] Guillaume Lample,et al. Unsupervised Translation of Programming Languages , 2020, NeurIPS.
[23] Mark Chen,et al. Language Models are Few-Shot Learners , 2020, NeurIPS.
[24] Nicolas Usunier,et al. End-to-End Object Detection with Transformers , 2020, ECCV.
[25] Christopher Ré,et al. Machine Learning on Graphs: A Model and Comprehensive Taxonomy , 2020, J. Mach. Learn. Res..
[26] Ting Liu,et al. CodeBERT: A Pre-Trained Model for Programming and Natural Languages , 2020, FINDINGS.
[27] Aditya Kanade,et al. Learning and Evaluating Contextual Embedding of Source Code , 2019, ICML.
[28] Marc Brockschmidt,et al. CodeSearchNet Challenge: Evaluating the State of Semantic Code Search , 2019, ArXiv.
[29] Shangqing Liu,et al. Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks , 2019, NeurIPS.
[30] David Lo,et al. Deep code comment generation with hybrid lexical and syntactical information , 2019, Empirical Software Engineering.
[31] Gabriele Bavota,et al. An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation , 2018, ACM Trans. Softw. Eng. Methodol..
[32] Christian Bird,et al. Deep learning type inference , 2018, ESEC/SIGSOFT FSE.
[33] Graham Neubig,et al. Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow , 2018, 2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR).
[34] Dawn Xiaodong Song,et al. Tree-to-tree Neural Networks for Program Translation , 2018, NeurIPS.
[35] Gianluca Palermo,et al. A Survey on Compiler Autotuning using Machine Learning , 2018, ACM Comput. Surv..
[36] Markus Schordan,et al. DataRaceBench: A Benchmark Suite for Systematic Evaluation of Data Race Detection Tools , 2017, SC17: International Conference for High Performance Computing, Networking, Storage and Analysis.
[37] Premkumar T. Devanbu,et al. A Survey of Machine Learning for Big Code and Naturalness , 2017, ACM Comput. Surv..
[38] Chris Cummins,et al. End-to-End Deep Learning of Optimization Heuristics , 2017, 2017 26th International Conference on Parallel Architectures and Compilation Techniques (PACT).
[39] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[40] Martin T. Vechev,et al. Probabilistic model for code with decision trees , 2016, OOPSLA.
[41] Anh Tuan Nguyen,et al. Divide-and-Conquer Approach for Multi-phase Statistical Migration for Source Code (T) , 2015, 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[42] Chanchal Kumar Roy,et al. Towards a Big Data Curated Benchmark of Inter-project Code Clones , 2014, 2014 IEEE International Conference on Software Maintenance and Evolution.
[43] Tao Wang,et al. Convolutional Neural Networks over Tree Structures for Programming Language Processing , 2014, AAAI.
[44] Michael D. Ernst,et al. Defects4J: a database of existing faults to enable controlled testing studies for Java programs , 2014, ISSTA 2014.
[45] Charles A. Sutton,et al. Mining source code repositories at massive scale using language modeling , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).
[46] Torsten Hoefler,et al. ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations , 2021, ICML.
[47] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[48] Alec Radford,et al. Improving Language Understanding by Generative Pre-Training , 2018 .
[49] Alvin Cheung,et al. Mapping Language to Code in Programmatic Context , 2018, EMNLP.