Articles de revues sur le sujet « AutoDL »
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Bang, Chang Seok, Hyun Lim, Hae Min Jeong, and Sung Hyeon Hwang. "Use of Endoscopic Images in the Prediction of Submucosal Invasion of Gastric Neoplasms: Automated Deep Learning Model Development and Usability Study." Journal of Medical Internet Research 23, no. 4 (2021): e25167. http://dx.doi.org/10.2196/25167.
Texte intégralChen, Yi-Wei, Qingquan Song, and Xia Hu. "Techniques for Automated Machine Learning." ACM SIGKDD Explorations Newsletter 22, no. 2 (2021): 35–50. http://dx.doi.org/10.1145/3447556.3447567.
Texte intégralChien, Ching-Syuan. "AutoDL-based convolutional neural networks for wildfire detection." Applied and Computational Engineering 18, no. 1 (2023): 134–42. http://dx.doi.org/10.54254/2755-2721/18/20230978.
Texte intégralTuggener, Lukas, Mohammadreza Amirian, Fernando Benites, et al. "Design Patterns for Resource-Constrained Automated Deep-Learning Methods." AI 1, no. 4 (2020): 510–38. http://dx.doi.org/10.3390/ai1040031.
Texte intégralZimmer, Lucas, Marius Lindauer, and Frank Hutter. "Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 9 (2021): 3079–90. http://dx.doi.org/10.1109/tpami.2021.3067763.
Texte intégralLiu, Zhengying, Adrien Pavao, Zhen Xu, et al. "Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 9 (2021): 3108–25. http://dx.doi.org/10.1109/tpami.2021.3075372.
Texte intégralPreuveneers, Davy. "AutoFL: Towards AutoML in a Federated Learning Context." Applied Sciences 13, no. 14 (2023): 8019. http://dx.doi.org/10.3390/app13148019.
Texte intégralWang, Wenbo, and Chengyou Lei. "Training a Minesweeper Agent Using a Convolutional Neural Network." Applied Sciences 15, no. 5 (2025): 2490. https://doi.org/10.3390/app15052490.
Texte intégralChen, Xu, and Brett Wujek. "AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3537–44. http://dx.doi.org/10.1609/aaai.v34i04.5759.
Texte intégralParker-Holder, Jack, Raghu Rajan, Xingyou Song, et al. "Automated Reinforcement Learning (AutoRL): A Survey and Open Problems." Journal of Artificial Intelligence Research 74 (June 1, 2022): 517–68. http://dx.doi.org/10.1613/jair.1.13596.
Texte intégralPatel, Kush. "AutoML and Automated Data Science by Democratizing AI through End-to-End Automation." International Journal for Research in Applied Science and Engineering Technology 12, no. 10 (2024): 494–504. http://dx.doi.org/10.22214/ijraset.2024.64555.
Texte intégralCao, Longbing. "Beyond AutoML: Mindful and Actionable AI and AutoAI With Mind and Action." IEEE Intelligent Systems 37, no. 5 (2022): 6–18. http://dx.doi.org/10.1109/mis.2022.3207860.
Texte intégralLan, Hai, Yuanjia Zhang, Zhifeng Bao, et al. "AutoDI." Proceedings of the VLDB Endowment 15, no. 12 (2022): 3626–29. http://dx.doi.org/10.14778/3554821.3554860.
Texte intégralYakovlev, Anatoly, Hesam Fathi Moghadam, Ali Moharrer, et al. "Oracle AutoML." Proceedings of the VLDB Endowment 13, no. 12 (2020): 3166–80. http://dx.doi.org/10.14778/3415478.3415542.
Texte intégralTornede, Tanja, Alexander Tornede, Jonas Hanselle, Felix Mohr, Marcel Wever, and Eyke Hüllermeier. "Towards Green Automated Machine Learning: Status Quo and Future Directions." Journal of Artificial Intelligence Research 77 (June 12, 2023): 427–57. http://dx.doi.org/10.1613/jair.1.14340.
Texte intégralThongprayoon, Charat, Pattharawin Pattharanitima, Andrea G. Kattah, et al. "Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury." Journal of Clinical Medicine 11, no. 21 (2022): 6264. http://dx.doi.org/10.3390/jcm11216264.
Texte intégralMustafa, Akram, and Mostafa Rahimi Azghadi. "Automated Machine Learning for Healthcare and Clinical Notes Analysis." Computers 10, no. 2 (2021): 24. http://dx.doi.org/10.3390/computers10020024.
Texte intégralSchlicher, Max, and Klaus Möller. "AutoML im Controlling." Controlling 34, no. 2 (2022): 39–42. http://dx.doi.org/10.15358/0935-0381-2022-2-39.
Texte intégralZender, Alexander, and Bernhard G. Humm. "Benchmarking Meta AutoML." Procedia Computer Science 256 (2025): 130–41. https://doi.org/10.1016/j.procs.2025.02.105.
Texte intégralThirunavukarasu, Arun James, Kabilan Elangovan, Laura Gutierrez, et al. "Clinical performance of automated machine learning: A systematic review." Annals of the Academy of Medicine, Singapore 53, no. 3 - Correct DOI (2024): 187–207. http://dx.doi.org/10.47102/annals-acadmedsg.2023113.
Texte intégralThirunavukarasu, Arun James, Kabilan Elangovan, Laura Gutierrez, et al. "Clinical performance of automated machine learning: A systematic review." Annals of the Academy of Medicine, Singapore 53, no. 3 (2024): 187–207. http://dx.doi.org/10.47102/https://doi.org/10.47102/annals-acadmedsg.2023113.
Texte intégralZöller, Marc-André, and Marco F. Huber. "Benchmark and Survey of Automated Machine Learning Frameworks." Journal of Artificial Intelligence Research 70 (January 27, 2021): 409–72. http://dx.doi.org/10.1613/jair.1.11854.
Texte intégralLazebnik, Teddy, Tzach Fleischer, and Amit Yaniv-Rosenfeld. "Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks." Sustainability 15, no. 14 (2023): 11232. http://dx.doi.org/10.3390/su151411232.
Texte intégralShujaat, Sohaib. "Automated Machine Learning in Dentistry: A Narrative Review of Applications, Challenges, and Future Directions." Diagnostics 15, no. 3 (2025): 273. https://doi.org/10.3390/diagnostics15030273.
Texte intégralTian, Junchi, and Chang Che. "Automated Machine Learning: A Survey of Tools and Techniques." Journal of Industrial Engineering and Applied Science 2, no. 6 (2024): 71–76. https://doi.org/10.70393/6a69656173.323336.
Texte intégralRosário, Albérico Travassos, and Anna Carolina Boechat. "How Automated Machine Learning Can Improve Business." Applied Sciences 14, no. 19 (2024): 8749. http://dx.doi.org/10.3390/app14198749.
Texte intégralKADIOGLU, Muhammet Ali. "End-to-End AutoML Implementation Framework." Eurasia Proceedings of Science Technology Engineering and Mathematics 19 (December 14, 2022): 35–40. http://dx.doi.org/10.55549/epstem.1218713.
Texte intégralLazebnik, Teddy, Amit Somech, and Abraham Itzhak Weinberg. "SubStrat." Proceedings of the VLDB Endowment 16, no. 4 (2022): 772–80. http://dx.doi.org/10.14778/3574245.3574261.
Texte intégralLiu, Sijia, Parikshit Ram, Deepak Vijaykeerthy, et al. "An ADMM Based Framework for AutoML Pipeline Configuration." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4892–99. http://dx.doi.org/10.1609/aaai.v34i04.5926.
Texte intégralLi, Yang, Yu Shen, Wentao Zhang, et al. "VolcanoML." Proceedings of the VLDB Endowment 14, no. 11 (2021): 2167–76. http://dx.doi.org/10.14778/3476249.3476270.
Texte intégralTOPSAKAL, Oguzhan, and Tahir Cetin AKINCI. "Classification and Regression Using Automatic Machine Learning (AutoML) – Open Source Code for Quick Adaptation and Comparison." Balkan Journal of Electrical and Computer Engineering 11, no. 3 (2023): 257–61. http://dx.doi.org/10.17694/bajece.1312764.
Texte intégralWeerts, Hilde, Florian Pfisterer, Matthias Feurer, et al. "Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML." Journal of Artificial Intelligence Research 79 (February 17, 2024): 639–77. http://dx.doi.org/10.1613/jair.1.14747.
Texte intégralCastellanos-Nieves, Dagoberto, and Luis García-Forte. "Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence." Applied Sciences 14, no. 14 (2024): 6196. http://dx.doi.org/10.3390/app14146196.
Texte intégralHelali, Mossad, Essam Mansour, Ibrahim Abdelaziz, Julian Dolby, and Kavitha Srinivas. "A scalable AutoML approach based on graph neural networks." Proceedings of the VLDB Endowment 15, no. 11 (2022): 2428–36. http://dx.doi.org/10.14778/3551793.3551804.
Texte intégralPaladino, Lauren M., Alexander Hughes, Alexander Perera, Oguzhan Topsakal, and Tahir Cetin Akinci. "Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction." AI 4, no. 4 (2023): 1036–58. http://dx.doi.org/10.3390/ai4040053.
Texte intégralJin, Zhengyang. "Exploring the Advancements and Challenges of Automated Machine Learning." Applied and Computational Engineering 8, no. 1 (2023): 732–37. http://dx.doi.org/10.54254/2755-2721/8/20230095.
Texte intégralThirunagalingam, Arunkumar. "Transforming Real-Time Data Processing: The Impact of AutoML on Dynamic Data Pipelines." FMDB Transactions on Sustainable Intelligent Networks 1, no. 2 (2024): 110–19. http://dx.doi.org/10.69888/ftsin.2024.000213.
Texte intégralSwapna Reddy Anugu. "Democratizing AI: How AutoML is transforming enterprise cloud strategies." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 701–8. https://doi.org/10.30574/wjaets.2025.15.1.0275.
Texte intégralKang, Sungmin, Gabin An, and Shin Yoo. "A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization." Proceedings of the ACM on Software Engineering 1, FSE (2024): 1424–46. http://dx.doi.org/10.1145/3660771.
Texte intégralYu, Chenyan, Yao Li, Minyue Yin, et al. "Automated Machine Learning in Predicting 30-Day Mortality in Patients with Non-Cholestatic Cirrhosis." Journal of Personalized Medicine 12, no. 11 (2022): 1930. http://dx.doi.org/10.3390/jpm12111930.
Texte intégralBodini, Matteo. "Daily Streamflow Forecasting Using AutoML and Remote-Sensing-Estimated Rainfall Datasets in the Amazon Biomes." Signals 5, no. 4 (2024): 659–89. http://dx.doi.org/10.3390/signals5040037.
Texte intégralDurmaz Engin, Ceren, Mahmut Ozan Gokkan, Seher Koksaldi, et al. "Comparing Auto-Machine Learning and Expert-Designed Models in Diagnosing Vitreomacular Interface Disorders." Journal of Clinical Medicine 14, no. 8 (2025): 2774. https://doi.org/10.3390/jcm14082774.
Texte intégralKoh, Joshua C. O., German Spangenberg, and Surya Kant. "Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping." Remote Sensing 13, no. 5 (2021): 858. http://dx.doi.org/10.3390/rs13050858.
Texte intégralLenkala, Swetha, Revathi Marry, Susmitha Reddy Gopovaram, Tahir Cetin Akinci, and Oguzhan Topsakal. "Comparison of Automated Machine Learning (AutoML) Tools for Epileptic Seizure Detection Using Electroencephalograms (EEG)." Computers 12, no. 10 (2023): 197. http://dx.doi.org/10.3390/computers12100197.
Texte intégralBosch, Nigel. "AutoML Feature Engineering for Student Modeling Yields High Accuracy, but Limited Interpretability." Journal of Educational Data Mining 13, no. 2 (2021): 55–79. https://doi.org/10.5281/zenodo.5275315.
Texte intégralCastellanos-Nieves, Dagoberto, and Luis García-Forte. "Improving Automated Machine-Learning Systems through Green AI." Applied Sciences 13, no. 20 (2023): 11583. http://dx.doi.org/10.3390/app132011583.
Texte intégralS. Vadar, Dr Parashuram, Dr Tejashree T. Moharekar, and Dr Urmila R. Pol. "COMPARATIVE ANALYSIS OF AUTOMATED MACHINE LEARNING LIBRARIES: PYCARET, H2O, TPOT, AUTO-SKLEARN, AND FLAML." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–8. http://dx.doi.org/10.55041/ijsrem39119.
Texte intégralLiam Connor, Rebecca Kelly, Saoirse Murphy, Eoghan McCarthy, and Edward Murray. "Analysis of AutoML Tools in the World of Automated Deep Learning." Fusion of Multidisciplinary Research, An International Journal 5, no. 1 (2024): 541–55. https://doi.org/10.63995/fxpc8243.
Texte intégralCasapu, Cristina-Ioana, and Simona Moldovanu. "Classification of Microorganism Using Convolutional Neural Network and H2O AutoML." SYSTEM THEORY, CONTROL AND COMPUTING JOURNAL 4, no. 1 (2024): 15–21. http://dx.doi.org/10.52846/stccj.2024.4.1.60.
Texte intégralSingpai, Bodin, and Desheng Wu. "Using a DEA–AutoML Approach to Track SDG Achievements." Sustainability 12, no. 23 (2020): 10124. http://dx.doi.org/10.3390/su122310124.
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