Artykuły w czasopismach na temat „Learning with pre-built models”
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Fauszt, Tibor, László Bognár i Ágnes Sándor. "Increasing the Prediction Power of Moodle Machine Learning Models with Self-defined Indicators". International Journal of Emerging Technologies in Learning (iJET) 16, nr 24 (21.12.2021): 23–39. http://dx.doi.org/10.3991/ijet.v16i24.23923.
Pełny tekst źródłaErian, Karim H., Pedro H. Regalado i James M. Conrad. "Missing data handling for machine learning models". IAES International Journal of Robotics and Automation (IJRA) 10, nr 2 (1.06.2021): 123. http://dx.doi.org/10.11591/ijra.v10i2.pp123-132.
Pełny tekst źródłaCinar, Eyup. "A Sensor Fusion Method Using Transfer Learning Models for Equipment Condition Monitoring". Sensors 22, nr 18 (8.09.2022): 6791. http://dx.doi.org/10.3390/s22186791.
Pełny tekst źródłaLage, Isaac, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Samuel J. Gershman i Finale Doshi-Velez. "Human Evaluation of Models Built for Interpretability". Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 7 (28.10.2019): 59–67. http://dx.doi.org/10.1609/hcomp.v7i1.5280.
Pełny tekst źródłaYimam, Seid Muhie, Abinew Ali Ayele, Gopalakrishnan Venkatesh, Ibrahim Gashaw i Chris Biemann. "Introducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets". Future Internet 13, nr 11 (27.10.2021): 275. http://dx.doi.org/10.3390/fi13110275.
Pełny tekst źródłaWang, Chengyu, Mengli Cheng, Xu Hu i Jun Huang. "EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 18 (18.05.2021): 16111–13. http://dx.doi.org/10.1609/aaai.v35i18.18028.
Pełny tekst źródłaSingh, Hrithik, Shambhavi Kaushik, Shruti Talyan i Kartikeya Dwivedi. "Skin Cancer Detection Using Deep Learning techniques". International Journal for Research in Applied Science and Engineering Technology 10, nr 5 (31.05.2022): 4296–305. http://dx.doi.org/10.22214/ijraset.2022.43090.
Pełny tekst źródłaPedditzi, Maria L., i Marcello Nonnis. "Pre-service Teachers' Representations About Children's Learning: A Pilot Study". Open Psychology Journal 13, nr 1 (13.11.2020): 315–20. http://dx.doi.org/10.2174/1874350102013010315.
Pełny tekst źródłaZhang, Li, Haimeng Fan, Chengxia Peng, Guozheng Rao i Qing Cong. "Sentiment Analysis Methods for HPV Vaccines Related Tweets Based on Transfer Learning". Healthcare 8, nr 3 (28.08.2020): 307. http://dx.doi.org/10.3390/healthcare8030307.
Pełny tekst źródłaAchicanoy, Harold, Deisy Chaves i Maria Trujillo. "StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications". Symmetry 13, nr 8 (16.08.2021): 1497. http://dx.doi.org/10.3390/sym13081497.
Pełny tekst źródłaMars, Mourad. "From Word Embeddings to Pre-Trained Language Models: A State-of-the-Art Walkthrough". Applied Sciences 12, nr 17 (1.09.2022): 8805. http://dx.doi.org/10.3390/app12178805.
Pełny tekst źródłaAn, Yaxin, i Sanket A. Deshmukh. "Machine learning approach for accurate backmapping of coarse-grained models to all-atom models". Chemical Communications 56, nr 65 (2020): 9312–15. http://dx.doi.org/10.1039/d0cc02651d.
Pełny tekst źródłaPallepati, Manvith. "Network Intrusion Detection System Using Machine Learning with Data Preprocessing and Feature Extraction". International Journal for Research in Applied Science and Engineering Technology 10, nr 6 (30.06.2022): 2360–65. http://dx.doi.org/10.22214/ijraset.2022.44326.
Pełny tekst źródłaOh, Seungmin, Sangwon Oh, Hyeju Shin i Jinsul Kim. "Pre-processing optimization for learning power consumption prediction models". Journal of Contents Computing 4, nr 1 (30.06.2022): 431–37. http://dx.doi.org/10.9728/jcc.2022.06.4.1.431.
Pełny tekst źródłaIyer-Raniga, Usha, i Pekka Huovila. "Learning through sharing: beyond the traditional North-South learning models for a circular built environment". IOP Conference Series: Earth and Environmental Science 588 (21.11.2020): 022023. http://dx.doi.org/10.1088/1755-1315/588/2/022023.
Pełny tekst źródłaKadam, Vaibhav, Satish Kumar, Arunkumar Bongale, Seema Wazarkar, Pooja Kamat i Shruti Patil. "Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products". Applied System Innovation 4, nr 2 (14.05.2021): 34. http://dx.doi.org/10.3390/asi4020034.
Pełny tekst źródłaEkung, Samuel, i Isaac Odesola. "LEARNING MODELS FOR EFFECTIVE PROPAGATION OF SUSTAINABLE CONSTRUCTION PRACTICES IN THE BUILT ENVIRONMENT". Malaysian Journal of Sustainable Environment 4, nr 1 (30.09.2018): 113. http://dx.doi.org/10.24191/myse.v4i1.5610.
Pełny tekst źródłaFahey-Gilmour, J., B. Dawson, P. Peeling, J. Heasman i B. Rogalski. "Multifactorial analysis of factors influencing elite Australian football match outcomes: a machine learning approach". International Journal of Computer Science in Sport 18, nr 3 (1.12.2019): 100–124. http://dx.doi.org/10.2478/ijcss-2019-0020.
Pełny tekst źródłaToba, Hapnes, Hendra Bunyamin, Juan Elisha Widyaya, Christian Wibisono i Lucky Surya Haryadi. "Masking preprocessing in transfer learning for damage building detection". IAES International Journal of Artificial Intelligence (IJ-AI) 12, nr 2 (1.06.2023): 552. http://dx.doi.org/10.11591/ijai.v12.i2.pp552-559.
Pełny tekst źródłaQiao, Yanhua, Xiaolei Zhu i Haipeng Gong. "BERT-Kcr: prediction of lysine crotonylation sites by a transfer learning method with pre-trained BERT models". Bioinformatics 38, nr 3 (13.10.2021): 648–54. http://dx.doi.org/10.1093/bioinformatics/btab712.
Pełny tekst źródłaKubera, Elżbieta, Agnieszka Kubik-Komar, Krystyna Piotrowska-Weryszko i Magdalena Skrzypiec. "Deep Learning Methods for Improving Pollen Monitoring". Sensors 21, nr 10 (19.05.2021): 3526. http://dx.doi.org/10.3390/s21103526.
Pełny tekst źródłaAnjomshoaa, Amin, i Edward Curry. "Transfer Learning in Smart Environments". Machine Learning and Knowledge Extraction 3, nr 2 (29.03.2021): 318–32. http://dx.doi.org/10.3390/make3020016.
Pełny tekst źródłaKhalil, Enas A. H., Enas M. F. El Houby i Hoda K. Mohamed. "Machine Learning for Multilabel Emotion Classification in Arabic Tweets". International Journal of Computer Science and Mobile Computing 11, nr 5 (30.05.2022): 222–33. http://dx.doi.org/10.47760/ijcsmc.2022.v11i05.010.
Pełny tekst źródłaHammad, Mahmoud, Mohammed Al-Smadi, Qanita Bani Baker i Sa’ad A. Al-Zboon. "Using deep learning models for learning semantic text similarity of Arabic questions". International Journal of Electrical and Computer Engineering (IJECE) 11, nr 4 (1.08.2021): 3519. http://dx.doi.org/10.11591/ijece.v11i4.pp3519-3528.
Pełny tekst źródłaOh, Seo Hyun, Min Kang i Youngho Lee. "Protected Health Information Recognition by Fine-Tuning a Pre-training Transformer Model". Healthcare Informatics Research 28, nr 1 (31.01.2022): 16–24. http://dx.doi.org/10.4258/hir.2022.28.1.16.
Pełny tekst źródłaZhao, Mingjun, Haijiang Wu, Di Niu i Xiaoli Wang. "Reinforced Curriculum Learning on Pre-Trained Neural Machine Translation Models". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 05 (3.04.2020): 9652–59. http://dx.doi.org/10.1609/aaai.v34i05.6513.
Pełny tekst źródłaReza, Ahmed Wasif, Md Mahamudul Hasan, Nazla Nowrin i Mir Moynuddin Ahmed Shibly. "Pre-trained deep learning models in automatic COVID-19 diagnosis". Indonesian Journal of Electrical Engineering and Computer Science 22, nr 3 (1.06.2021): 1540. http://dx.doi.org/10.11591/ijeecs.v22.i3.pp1540-1547.
Pełny tekst źródłaRotman, Guy, i Roi Reichart. "Multi-task Active Learning for Pre-trained Transformer-based Models". Transactions of the Association for Computational Linguistics 10 (2022): 1209–28. http://dx.doi.org/10.1162/tacl_a_00515.
Pełny tekst źródłaBaranov, Mykola, Yurii Shcherbyna i Oles Khodych. "Exploit computer vision inpainting approach to boost deep learning models". Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 12 (15.12.2022): 1–6. http://dx.doi.org/10.23939/sisn2022.12.001.
Pełny tekst źródłaKim, Gyunyeop, i Sangwoo Kang. "Effective Transfer Learning with Label-Based Discriminative Feature Learning". Sensors 22, nr 5 (4.03.2022): 2025. http://dx.doi.org/10.3390/s22052025.
Pełny tekst źródłaVerbruggen, Gust, Vu Le i Sumit Gulwani. "Semantic programming by example with pre-trained models". Proceedings of the ACM on Programming Languages 5, OOPSLA (20.10.2021): 1–25. http://dx.doi.org/10.1145/3485477.
Pełny tekst źródłaShadiev, Rustam, Xueying Wang, Ting-Ting Wu i Yueh-Min Huang. "Review of Research on Technology-Supported Cross-Cultural Learning". Sustainability 13, nr 3 (29.01.2021): 1402. http://dx.doi.org/10.3390/su13031402.
Pełny tekst źródłaWeng, Rongxiang, Heng Yu, Weihua Luo i Min Zhang. "Deep Fusing Pre-trained Models into Neural Machine Translation". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 10 (28.06.2022): 11468–76. http://dx.doi.org/10.1609/aaai.v36i10.21399.
Pełny tekst źródłaJeon, Joohyeong, Byungohk Rhee i Jinsu Gim. "Melt Temperature Estimation by Machine Learning Model Based on Energy Flow in Injection Molding". Polymers 14, nr 24 (18.12.2022): 5548. http://dx.doi.org/10.3390/polym14245548.
Pełny tekst źródłaBulla, Premamayudu, Lakshmipathi Anantha i Subbarao Peram. "Deep Neural Networks with Transfer Learning Model for Brain Tumors Classification". Traitement du Signal 37, nr 4 (10.10.2020): 593–601. http://dx.doi.org/10.18280/ts.370407.
Pełny tekst źródłaPatel, Tatsat R., Muhammad Waqas, Seyyed M. M. J. Sarayi, Zeguang Ren, Cesario V. Borlongan, Rimal Dossani, Elad I. Levy i in. "Revascularization Outcome Prediction for A Direct Aspiration-First Pass Technique (ADAPT) from Pre-Treatment Imaging and Machine Learning". Brain Sciences 11, nr 10 (5.10.2021): 1321. http://dx.doi.org/10.3390/brainsci11101321.
Pełny tekst źródłaB, Naveen Kumar, C. Akshay, Abhishek S P, Ganadakshaka K i K. Jayanth. "An Automated System for Fruit Adulteration and Fruit Grading". International Journal for Research in Applied Science and Engineering Technology 10, nr 6 (30.06.2022): 1223–27. http://dx.doi.org/10.22214/ijraset.2022.44011.
Pełny tekst źródłaKhalifa, Noorain, Leela Sarath Kumar Konda i Rajendra Kristam. "Machine learning-based QSAR models to predict sodium ion channel (Nav 1.5) blockers". Future Medicinal Chemistry 12, nr 20 (październik 2020): 1829–43. http://dx.doi.org/10.4155/fmc-2020-0156.
Pełny tekst źródłaAlelyani, Salem. "Detection and Evaluation of Machine Learning Bias". Applied Sciences 11, nr 14 (7.07.2021): 6271. http://dx.doi.org/10.3390/app11146271.
Pełny tekst źródłaDidkovskyi, Oleksandr, Vladislav Ivanov, Alessio Radice, Monica Papini, Laura Longoni i Alessandra Menafoglio. "A Comparison Between Machine Learning and Functional Geostatistics Approaches for Data-Driven Analyses of Sediment Transport in a Pre-Alpine Stream". Mathematical Geosciences 54, nr 3 (16.03.2022): 467–506. http://dx.doi.org/10.1007/s11004-022-09995-9.
Pełny tekst źródłaBechelli, Solene, i Jerome Delhommelle. "Machine Learning and Deep Learning Algorithms for Skin Cancer Classification from Dermoscopic Images". Bioengineering 9, nr 3 (27.02.2022): 97. http://dx.doi.org/10.3390/bioengineering9030097.
Pełny tekst źródłaLi, Y., Y. Tong, K. Lu, S. Yu i J. Qian. "P213 Can artificial intelligence help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neuron network". Journal of Crohn's and Colitis 14, Supplement_1 (styczeń 2020): S247—S248. http://dx.doi.org/10.1093/ecco-jcc/jjz203.342.
Pełny tekst źródłaIdrissi, Idriss, Mostafa Azizi i Omar Moussaoui. "Accelerating the update of a DL-based IDS for IoT using deep transfer learning". Indonesian Journal of Electrical Engineering and Computer Science 23, nr 2 (1.08.2021): 1059. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp1059-1067.
Pełny tekst źródłaBassier, M., M. Vergauwen i B. Van Genechten. "AUTOMATED CLASSIFICATION OF HERITAGE BUILDINGS FOR AS-BUILT BIM USING MACHINE LEARNING TECHNIQUES". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W2 (16.08.2017): 25–30. http://dx.doi.org/10.5194/isprs-annals-iv-2-w2-25-2017.
Pełny tekst źródłaSaraubon, Kobkiat, Nuttapong Wiriyanuruknakon i Natdanai Tangthirasunun. "Flashover Prevention System using IoT and Machine Learning for Transmission and Distribution Lines". International Journal of Interactive Mobile Technologies (iJIM) 15, nr 11 (4.06.2021): 34. http://dx.doi.org/10.3991/ijim.v15i11.20753.
Pełny tekst źródłaAbbas, Ather, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun i Kyung Hwa Cho. "AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods". Geoscientific Model Development 15, nr 7 (8.04.2022): 3021–39. http://dx.doi.org/10.5194/gmd-15-3021-2022.
Pełny tekst źródłaHuang, Yefei, Tianlai Xu, Zexu Zhang, Hutao Cui i Yu Su. "Satellite Segmentation with Pre-trained CNN Models". Journal of Physics: Conference Series 2171, nr 1 (1.01.2022): 012003. http://dx.doi.org/10.1088/1742-6596/2171/1/012003.
Pełny tekst źródłaMiyazawa, Kazuki, Yuta Kyuragi i Takayuki Nagai. "Simple and Effective Multimodal Learning Based on Pre-Trained Transformer Models". IEEE Access 10 (2022): 29821–33. http://dx.doi.org/10.1109/access.2022.3159346.
Pełny tekst źródłaShoaib, Mohamed R., Mohamed R. Elshamy, Taha E. Taha, Adel S. El-Fishawy i Fathi E. Abd El-Samie. "Efficient Brain Tumor Detection Based on Deep Learning Models". Journal of Physics: Conference Series 2128, nr 1 (1.12.2021): 012012. http://dx.doi.org/10.1088/1742-6596/2128/1/012012.
Pełny tekst źródłaDang, Cach N., María N. Moreno-García i Fernando De la Prieta. "Hybrid Deep Learning Models for Sentiment Analysis". Complexity 2021 (12.08.2021): 1–16. http://dx.doi.org/10.1155/2021/9986920.
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