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Towards Prediction of Turbulent Flows at High Reynolds Numbers Using High Performance Computing Data and Deep Learning., , , , , and . ISC Workshops, volume 11203 of Lecture Notes in Computer Science, page 614-623. Springer, (2018)Unifying Input and Output Smoothing in Neural Machine Translation., , and . COLING, page 4361-4372. International Committee on Computational Linguistics, (2020)ApiQ: Finetuning of 2-Bit Quantized Large Language Model., and . CoRR, (2024)Mask More and Mask Later: Efficient Pre-training of Masked Language Models by Disentangling the MASK Token., , , , and . EMNLP (Findings), page 1478-1492. Association for Computational Linguistics, (2022)Multi-Agent Mutual Learning at Sentence-Level and Token-Level for Neural Machine Translation., , and . EMNLP (Findings), volume EMNLP 2020 of Findings of ACL, page 1715-1724. Association for Computational Linguistics, (2020)Parameter-Efficient Fine-Tuning without Introducing New Latency., , and . ACL (1), page 4242-4260. Association for Computational Linguistics, (2023)Make Your Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning., , and . CoRR, (2023)Ask Language Model to Clean Your Noisy Translation Data., , , , and . EMNLP (Findings), page 3215-3236. Association for Computational Linguistics, (2023)Back-translation for Large-Scale Multilingual Machine Translation., , and . WMT@EMNLP, page 418-424. Association for Computational Linguistics, (2021)Is It a Free Lunch for Removing Outliers during Pretraining?, and . CoRR, (2024)