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Recent development in large language models (LLMs) has demonstrated impressive domain proficiency on unstructured textual or multi-modal tasks.
Using the adap learning algorithm to forecast the onset of diabetes mellitus
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Cardiovascular risk factors. insights from framingham heart study
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A critical comparative study of liver patients from usa and india: an exploratory analysis
Bendi Venkata Ramana, M Surendra Prasad Babu, and NB Venkateswarlu · 2012
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
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XGBoost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Mimic-iii, a freely accessible critical care database
Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark · 2016
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A closer look at memorization in deep networks
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Lightgbm: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama · 2018
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Using machine learning techniques to generate laboratory diagnostic pathways—a case study
Georg Hoffmann, Andreas Bietenbeck, Ralf Lichtinghagen, and Frank Klawonn · 2018
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Explainable prediction of medical codes from clinical text
James Mullenbach, Sarah Wiegreffe, Jon Duke, Jimeng Sun, and Jacob Eisenstein · 2018
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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
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Understanding and utilizing deep neural networks trained with noisy labels
Pengfei Chen, Ben Ben Liao, Guangyong Chen, and Shengyu Zhang · 2019
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Clinicalbert: Modeling clinical notes and predicting hospital readmission
Kexin Huang, Jaan Altosaar, and Rajesh Ranganath · 2019
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Churn modelling, 2019
Shruti Iyyer · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Dividemix: Learning with noisy labels as semi-supervised learning
Junnan Li, Richard Socher, and Steven CH Hoi · 2019
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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A new tool to predict lung cancer based on risk factors
Ahmad S Ahmad and Ali M Mayya · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Stroke prediction dataset
Fedesoriano · 2020
Transtab: Learning transferable tabular transformers across tables
Zifeng Wang and Jimeng Sun · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Elephants never forget: Testing language models for memorization of tabular data
Sebastian Bordt, Harsha Nori, and Rich Caruana · 2023
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Evaluating the feasibility of chatgpt in healthcare: an analysis of multiple clinical and research scenarios
Marco Cascella, Jonathan Montomoli, Valentina Bellini, and Elena Bignami · 2023
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TabCaps: A capsule neural network for tabular data classification with BoW routing
Jintai Chen, KuanLun Liao, Yanwen Fang, Danny Z. Chen, and Jian Wu · 2023
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Symptoms and covid presence (may 2020 data)
Hemanthhari · 2020
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Likelihood prediction of diabetes at early stage using data mining techniques
MM Islam, Rahatara Ferdousi, Sadikur Rahman, and Humayra Yasmin Bushra · 2020
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TabNet: Attentive interpretable tabular learning
Sercan Ö Arik and Tomas Pfister · 2021
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Revisiting deep learning models for tabular data
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2021
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Hybrid models based on genetic algorithm and deep learning algorithms for nutritional anemia disease classification
Serhat Kilicarslan, Mete Celik, and Şafak Sahin · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
Cited alongside, same era.
Aniket Deroy, Kripabandhu Ghosh, and Saptarshi Ghosh · 2023
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What indeed can gpt models do in chemistry? a comprehensive benchmark on eight tasks
Taicheng Guo, Kehan Guo, et al · 2023
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Tabllm: Few-shot classification of tabular data with large language models
Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, and David Sontag · 2023
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Tabpfn: A transformer that solves small tabular classification problems in a second
Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter · 2023
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Can generalist foundation models outcompete special-purpose tuning? case study in medicine
Harsha Nori, Yin Tat Lee, Sheng Zhang, Dean Carignan, Richard Edgar, Nicolo Fusi, Nicholas King, Jonathan Larson, Yuanzhi Li, Weishung Liu, et al · 2023
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Gpt-4 technical report
OpenAI · 2023
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Limitations of language models in arithmetic and symbolic induction
Jing Qian, Hong Wang, Zekun Li, Shiyang Li, and Xifeng Yan · 2023
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Toolllm: Facilitating large language models to master 16000+ real-world apis
Yujia Qin, Shihao Liang, et al · 2023
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2023
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T2G-Former: Organizing tabular features into relation graphs promotes heterogeneous feature interaction
Jiahuan Yan, Jintai Chen, Yixuan Wu, Danny Z Chen, and Jian Wu · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
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Large language models meet nl2code: A survey
Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Wang Yongji, and Jian-Guang Lou · 2023
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, et al · 2023
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Can a deep learning model be a sure bet for tabular prediction?
Jintai Chen, Jiahuan Yan, Qiyuan Chen, Danny Z Chen, Jian Wu, and Jimeng Sun · 2024
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From supervised to generative: A novel paradigm for tabular deep learning with large language models
Xumeng Wen, Han Zhang, Shun Zheng, Wei Xu, and Jiang Bian · 2024
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