Improving obstetric artificial intelligence through the background linguistic comments

  • Brown, T. And others. Language models are few learners. ADV. Nervousness. practical. split. 331877-1901 (2020).

  • Trinh, th, wu, y., le, QV, He, H. & Luong, T. Solving Olympiad Heygetry without human demonstrations. nature 625476-482 (2024).

    condition
    Ads
    cup
    PubMed
    Pubmed Central

    The scientific researcher from Google

  • To me, j. And others. Getting the code at the level of competition with AlPhacode. sciences 3781092-1097 (2022).

    condition
    Ads
    cup
    PubMed
    mathematics

    The scientific researcher from Google

  • Yang, c. And others. SWE-Agent: Computer Software Engineering allows mechanism. in ADV. Nervousness. practical. split. 37 (NeuPIPs, 2024).

  • Khattab, O. et al. DSPY: Collecting the introductory language model calls for modern pipelines. in The twelfth international conference on learning representations (2024).

  • Zhaaria, M. Et al. Transformation from models to vehicle AI systems. Bayer https://bair.berkeleg (2024).

  • Zhou, y. et al. Big language models are engineers directed at the human level. in The eleventh international conference on learning representations (2023).

  • Krizhevsky, A., Sutskever, I. & Hinton, GE Imagenet Classification with Deep Tunus Neural Networks. in ADV. Nervousness. practical. split. 25 (Neurips, 2012).

  • Jumper, J. et al. Predicting with a very accurate protein structure with alphafold. nature 596583-589 (2021).

    condition
    Ads
    cup
    PubMed
    Pubmed Central
    mathematics

    The scientific researcher from Google

  • Fawzi, A. Et al. Discovering double -rated algorithms faster with reinforcement learning. nature 61047-53 (2022).

    condition
    Ads
    cup
    PubMed
    Pubmed Central
    mathematics

    The scientific researcher from Google

  • Mankowitz, Dj Et al. Faster sorting algorithms discovered using deep reinforcement learning. nature 618257-263 (2023).

    condition
    Ads
    cup
    PubMed
    Pubmed Central
    mathematics

    The scientific researcher from Google

  • The merchant, a. And others. Deep learning limiting to discover materials. nature 62480-85 (2023).

    condition
    Ads
    cup
    PubMed
    Pubmed Central
    mathematics

    The scientific researcher from Google

  • Goodfellow, I., Bengio, Y. & Couourville, A. Deep learning (With Journalism, 2016).

  • Rumelhart, De, Hinton, Ge & Williams, RJ Learning representations through decline errors. nature 323533-536 (1986).

    condition
    Ads
    mathematics

    The scientific researcher from Google

  • Brezant, R. And others. Automatic directed improvement with “gradient” and searching for a beam. in Brook. 2023 Conference on Experimental Methods in Treating Natural Language (Eds Bouamor, H. Et Al

  • Zheng, L. et al. Llm-AS-A-Dugy with MH-Bencer and Chatbot Arena. ADV. Nervousness. practical. split. 3646595-46623 (2023).

  • Li, x. et al. Alpacaeval: Automatic Restriction of Instruction Models. Gyrroup https://github.com/tatsu-lab/alpaca_eval (2023).

  • Bay, j. And others. Useful and harmless assistant training with learning to enhance human reactions. Preprint in https://arxiv.org/abs/2204.05862 (2022).

  • Madaan, A. Et al. Self -reverse: repetitive improvement with self -nutrition. in ADV. Nervousness. practical. split. 36 (NEUPS, 2023).

  • Stiennon, N. Et al. Learn to summarize with human reactions. A ADV. Nervousness. practical. split. 333008-3021 (2020).

  • Yuan, W. and others. Language models that are good at the self. in The forty -first international conference on machine learning (2024).

  • Dubois, y. et al. Alpacafarm: Simulating frameworks that learn from human reactions. in ADV. Nervousness. practical. split. 36 (NEUPS, 2023).

  • Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K. & Yao, S. ADV. Nervousness. practical. split. 368634-8652 (2023).

  • Rin, d. And others. GPQA: Standard of Questions and Resistant Answers at the level of graduates. in The first conference of language modeling (2024).

  • Hendrycks, D. et al. Measuring the understanding of a massive multi -task language. in The ninth international conference on learning representations (2021).

  • Lu, b. And others. Mathvista: Evaluating mathematical thinking of the basic models in visual contexts. in The twelfth international conference on learning representations (2024).

  • Lu, b. And others. Learn to explain: multimedia thinking through thought chains to answer science questions. ADV. Nervousness. practical. split. 352507-2521 (2022).

  • LIU, p. et al. Pre -training, claim, and prediction: a systematic survey of methods of methods in treating natural language. ACM Comput. Survival. 551-35 (2023).

    mathematics

    The scientific researcher from Google

  • Suzgun, M. Et al. Challenging great tasks in the large term and whether the idea series can be solved. in Results of the Association of Mass: ACL 2023 13003-13051 (Association of Mass, 2023).

  • Cobbe, K. et al. Training challenges to solve mathematics lyric problems. Preprint in https://arxiv.org/abs/2110.14168 (2021).

  • Yang, C and others. Great language models as improved. in The twelfth international conference on learning representations (2024).

  • Dubey, A. Et al. Lama cap 3 of the models. Preprint in https://arxiv.org/abs/2407.21783 (2024).

  • Yang, a. And others. QWEN2 Technical Report. Preprint in https://arxiv.org/abs/2407.10671 (2024).

  • Khan, FM, Gibbons, JP & Spertuto, PW Planning of Khan treatment in radiology (Lippincott Williams & Wilkins (Wolters Kluweer), 2016).

  • Hussein, M. Br. J. Radiol. 9120180270 (2018).

    condition
    PubMed
    Pubmed Central

    The scientific researcher from Google

  • Kisling, K. et al. Radiation Planning Assistant, A, fully automated radiotherapy system. J. Vis. Earn. 134E57411 (2018).

  • Huang, C., Nomura, Y., Yang, Y. & Xing, L. Meta-Optimization for completely automated radiotherapy. physics. Med. Biol. 67055011 (2022).

    condition
    cup
    mathematics

    The scientific researcher from Google

  • Yang, Y. & Xing, L. Clinical Clinical Roverse Treatment Planning. physics. Med. Biol. 495101 (2004).

    condition
    PubMed
    mathematics

    The scientific researcher from Google

  • Liu, S. Et al. Automatic radiotherapy layout is guided by GPT-4vision. Preprint in https://arxiv.org/abs/2406.15609 (2024).

  • Lu, b. And others. Charmeleon: The syntactic logic of delivery and operation with large language models. ADV. Nervousness. practical. split. 3643447-43478 (2023).

  • Yan, b. , Chang, c. Yuan, g. Preprint in https://arxiv.org/abs/2406.17115 (2024).

  • Wei, J. et al. The series of idea that provokes thinking about large language models. ADV. Nervousness. practical. split. 3524824-24837 (2022).

  • Bottou, L. Automatic learning widely with random gradient. in Brook. Compststat’2010 (Eds Lechevallier, Y. & Saporta, G.) 177–186 (Physica-Verlag, 2010).

  • Wang, S. And others. High -dimensional automated radiotherapy planning by improving Paisi. Med. physics. 503773-3787 (2023).

    condition
    PubMed
    mathematics

    The scientific researcher from Google

  • Akiba, T., Sano, S., Yanase, T., OHTA, T in Brook. ACM Sigkdd Fifth International Conference to discover knowledge and extract data (2019).

  • Pianchi, F. And others. Closed Group / Vital text: V0.1.6. Zenudo https://doi.org/10.5281/zenodo.14497017 (2024).

  • Leave a Comment