Accesso libero

Research on Machine Learning Program Generation Algorithm Based on AORBCO

INFORMAZIONI SU QUESTO ARTICOLO

Cita

Huang Liwei, Jiang Bitao, Lu Shouye et al. Review of recommendation systems based on Deep Learning [J]. Journal of Computers, 2018, 41(07):1619–1647. LiweiHuang BitaoJiang ShouyeLu Review of recommendation systems based on Deep Learning [J] Journal of Computers 2018 41 07 1619 1647 Search in Google Scholar

Beltramelli T. pix2code: Generating Code from a Graphical User Interface Screenshot [C]//Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems. 2018: 1–6. BeltramelliT pix2code: Generating Code from a Graphical User Interface Screenshot [C] Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems 2018 1 6 Search in Google Scholar

Ahmad W U, Chakraborty S, Ray B, et al. Unified Pre-training for Program Understanding and Generation [J]. 2021. DOI: 10.18653/v1/2021.naacl-main.211. AhmadW U ChakrabortyS RayB Unified Pre-training for Program Understanding and Generation [J] 2021 10.18653/v1/2021.naacl-main.211 Open DOISearch in Google Scholar

Wang Y, Wang W, Joty S, et al. CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation [J]. 2021. WangY WangW JotyS CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation [J] 2021 Search in Google Scholar

Raffel C, Shazeer N, Roberts A, et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer [J]. 2019. DOI: 10.48550/arXiv.1910.10683. RaffelC ShazeerN RobertsA Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer [J] 2019 10.48550/arXiv.1910.10683 Open DOISearch in Google Scholar

Feng Yanxing, Research on Program Generation in AORBCO Model [D]. Xi'an University of Technology, 2021. DOI: 10.27391/dcnki.gxagu.2021.000121 YanxingFeng Research on Program Generation in AORBCO Model [D] Xi'an University of Technology 2021 10.27391/dcnki.gxagu.2021.000121 Open DOISearch in Google Scholar

Xiao Liangshun, Research on Knowledge Fusion in AORBCO Modeling [D]. Xi'an University of Technology, 2023. LiangshunXiao Research on Knowledge Fusion in AORBCO Modeling [D] Xi'an University of Technology 2023 Search in Google Scholar

He X, Liao L, Zhang H, et al. Neural Collaborative Filtering [J]. International World Wide Web Conferences Steering Committee, 2017. DOI: 10.1145/3038912.3052569. HeX LiaoL ZhangH Neural Collaborative Filtering [J] International World Wide Web Conferences Steering Committee 2017 10.1145/3038912.3052569 Open DOISearch in Google Scholar

Pennington J, Socher R, Manning C. Glove: Global Vectors for Word Representation [J]. 2014. DOI: 10.3115/v1/D14-1162. PenningtonJ SocherR ManningC Glove: Global Vectors for Word Representation [J] 2014 10.3115/v1/D14-1162 Open DOISearch in Google Scholar

Rasley J, Rajbhandari S, Ruwase O, et al. DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters [C]//KDD'20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2020. DOI: 10.1145/3394486.3406703. RasleyJ RajbhandariS RuwaseO DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters [C] KDD'20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM 2020 10.1145/3394486.3406703 Open DOISearch in Google Scholar

Lewis P, Perez E, Piktus A, et al. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks [J]. 2020. DOI: 10.48550/arXiv.2005.11401. LewisP PerezE PiktusA Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks [J] 2020 10.48550/arXiv.2005.11401 Open DOISearch in Google Scholar

Karpukhin V, Ouz B, Min S, et al. Dense Passage Retrieval for Open-Domain Question Answering [J]. 2020. DOI: 10.18653/v1/2020.emnlp-main.550 KarpukhinV OuzB MinS Dense Passage Retrieval for Open-Domain Question Answering [J] 2020 10.18653/v1/2020.emnlp-main.550 Open DOISearch in Google Scholar

Izacard G, Grave E. Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering [J]. 2020. DOI: 10.48550/arXiv.2007.01282. IzacardG GraveE Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering [J] 2020 10.48550/arXiv.2007.01282 Open DOISearch in Google Scholar

Wang H, Zhang F, Zhang M, et al. Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems [J]. SIGKDD explorations, 2019. WangH ZhangF ZhangM Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems [J] SIGKDD explorations 2019 Search in Google Scholar

Li Xiang, Yang Xingyao, Yu Jiong et al. A bipartite recommendation algorithm based on knowledge graph convolutional networks [J]. Computer Science and Exploration, 2022, 16(01):176–184. XiangLi XingyaoYang JiongYu A bipartite recommendation algorithm based on knowledge graph convolutional networks [J] Computer Science and Exploration 2022 16 01 176 184 Search in Google Scholar

Ren S, Guo D, Lu S, et al. CodeBLEU: a Method for Automatic Evaluation of Code Synthesis [J]. 2020. DOI: 10.48550/arXiv.2009.10297. RenS GuoD LuS CodeBLEU: a Method for Automatic Evaluation of Code Synthesis [J] 2020 10.48550/arXiv.2009.10297 Open DOISearch in Google Scholar

Barbella, Marcello and Tortora, Genoveffa, Rouge Metric Evaluation for Text Summarization Techniques. Available at SSRN: https://ssrn.com/abstract=4120317 BarbellaMarcello TortoraGenoveffa Rouge Metric Evaluation for Text Summarization Techniques Available at SSRN: https://ssrn.com/abstract=4120317 Search in Google Scholar

Ehud Reiter; A Structured Review of the Validity of BLEU. Computational Linguistics 2018; 44 (3): 393–401. doi: https://doi.org/10.1162/coli_a_00322 ReiterEhud A Structured Review of the Validity of BLEU Computational Linguistics 2018 44 3 393 401 doi: https://doi.org/10.1162/coli_a_00322 Search in Google Scholar

eISSN:
2470-8038
Lingua:
Inglese
Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Computer Sciences, other