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Domain-Specific Lexical Grounding in Noisy Visual-Textual Documents.

, , and . EMNLP (1), page 2039-2045. Association for Computational Linguistics, (2020)

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A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity., , , , , , , , , and 1 other author(s). CoRR, (2023)Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health., , , , , and . CoRR, (2018)Data Similarity is Not Enough to Explain Language Model Performance., , and . EMNLP, page 11295-11304. Association for Computational Linguistics, (2023)Computational Histological Staining and Destaining of Prostate Core Biopsy RGB Images with Generative Adversarial Neural Networks., , , and . ICMLA, page 828-834. IEEE, (2018)Comparing Text Representations: A Theory-Driven Approach., and . EMNLP (1), page 5527-5539. Association for Computational Linguistics, (2021)Reinforcement Learning with Action-Derived Rewards for Chemotherapy and Clinical Trial Dosing Regimen Selection., and . MLHC, volume 85 of Proceedings of Machine Learning Research, page 161-226. PMLR, (2018)Digital reconstruction of teeth using near-infrared light., , , , and . EMBC, page 4414-4418. IEEE, (2019)Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health., , , , , and . EMBC, page 3387-3393. IEEE, (2019)Domain-Specific Lexical Grounding in Noisy Visual-Textual Documents., , and . EMNLP (1), page 2039-2045. Association for Computational Linguistics, (2020)A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity., , , , , , , , , and 1 other author(s). NAACL-HLT, page 3245-3276. Association for Computational Linguistics, (2024)