Artículos de revistas sobre el tema "Benign overfitting"
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Bartlett, Peter L., Philip M. Long, Gábor Lugosi y Alexander Tsigler. "Benign overfitting in linear regression". Proceedings of the National Academy of Sciences 117, n.º 48 (24 de abril de 2020): 30063–70. http://dx.doi.org/10.1073/pnas.1907378117.
Texto completoPeters, Evan y Maria Schuld. "Generalization despite overfitting in quantum machine learning models". Quantum 7 (20 de diciembre de 2023): 1210. http://dx.doi.org/10.22331/q-2023-12-20-1210.
Texto completoBartlett, Peter L., Andrea Montanari y Alexander Rakhlin. "Deep learning: a statistical viewpoint". Acta Numerica 30 (mayo de 2021): 87–201. http://dx.doi.org/10.1017/s0962492921000027.
Texto completoWang, Ke y Christos Thrampoulidis. "Binary Classification of Gaussian Mixtures: Abundance of Support Vectors, Benign Overfitting, and Regularization". SIAM Journal on Mathematics of Data Science 4, n.º 1 (marzo de 2022): 260–84. http://dx.doi.org/10.1137/21m1415121.
Texto completoHu, Wei. "Understanding Surprising Generalization Phenomena in Deep Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 20 (24 de marzo de 2024): 22669. http://dx.doi.org/10.1609/aaai.v38i20.30285.
Texto completoMontaha, Sidratul, Sami Azam, A. K. M. Rakibul Haque Rafid, Sayma Islam, Pronab Ghosh y Mirjam Jonkman. "A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity". PLOS ONE 17, n.º 8 (4 de agosto de 2022): e0269826. http://dx.doi.org/10.1371/journal.pone.0269826.
Texto completoWindisch, Paul, Carole Koechli, Susanne Rogers, Christina Schröder, Robert Förster, Daniel R. Zwahlen y Stephan Bodis. "Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review". Cancers 14, n.º 11 (27 de mayo de 2022): 2676. http://dx.doi.org/10.3390/cancers14112676.
Texto completoLiang, ShuFen, HuiLin Liu, FangChen Yang, Chuanbo Qin y Yue Feng. "Classification of Benign and Malignant Pulmonary Nodules Using a Regularized Extreme Learning Machine". Journal of Medical Imaging and Health Informatics 11, n.º 8 (1 de agosto de 2021): 2117–23. http://dx.doi.org/10.1166/jmihi.2021.3448.
Texto completoLiu, Xinwei, Xiaojun Jia, Jindong Gu, Yuan Xun, Siyuan Liang y Xiaochun Cao. "Does Few-Shot Learning Suffer from Backdoor Attacks?" Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 18 (24 de marzo de 2024): 19893–901. http://dx.doi.org/10.1609/aaai.v38i18.29965.
Texto completoDoimo, Diego, Aldo Glielmo, Sebastian Goldt y Alessandro Laio. "Redundant representations help generalization in wide neural networks * , †". Journal of Statistical Mechanics: Theory and Experiment 2023, n.º 11 (1 de noviembre de 2023): 114011. http://dx.doi.org/10.1088/1742-5468/aceb4f.
Texto completoLi, Jian, Yong Liu y Weiping Wang. "High-Dimensional Analysis for Generalized Nonlinear Regression: From Asymptotics to Algorithm". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 12 (24 de marzo de 2024): 13500–13508. http://dx.doi.org/10.1609/aaai.v38i12.29253.
Texto completoAlkhaleefah, Mohammad, Shang-Chih Ma, Yang-Lang Chang, Bormin Huang, Praveen Kumar Chittem y Vishnu Priya Achhannagari. "Double-Shot Transfer Learning for Breast Cancer Classification from X-Ray Images". Applied Sciences 10, n.º 11 (9 de junio de 2020): 3999. http://dx.doi.org/10.3390/app10113999.
Texto completoIstighosah, Maie, Andi Sunyoto y Tonny Hidayat. "Breast Cancer Detection in Histopathology Images using ResNet101 Architecture". sinkron 8, n.º 4 (1 de octubre de 2023): 2138–49. http://dx.doi.org/10.33395/sinkron.v8i4.12948.
Texto completoAnjum, Sunila, Imran Ahmed, Muhammad Asif, Hanan Aljuaid, Fahad Alturise, Yazeed Yasin Ghadi y Rashad Elhabob. "Lung Cancer Classification in Histopathology Images Using Multiresolution Efficient Nets". Computational Intelligence and Neuroscience 2023 (16 de octubre de 2023): 1–12. http://dx.doi.org/10.1155/2023/7282944.
Texto completoNadkarni, Swati y Kevin Noronha. "Breast cancer detection using ensemble of convolutional neural networks". International Journal of Electrical and Computer Engineering (IJECE) 14, n.º 1 (1 de febrero de 2024): 1041. http://dx.doi.org/10.11591/ijece.v14i1.pp1041-1047.
Texto completoRen, Cheng y Shouming Hou. "A Hybrid Deep Learning Approach for Lung Nodule Classification". Frontiers in Computing and Intelligent Systems 8, n.º 1 (10 de mayo de 2024): 6–12. http://dx.doi.org/10.54097/498fxm65.
Texto completoZi Wei, Yee, Marina Md-Arshad, Adlina Abdul Samad y Norafida Ithnin. "Comparing Malware Attack Detection using Machine Learning Techniques in IoT Network Traffic". International Journal of Innovative Computing 13, n.º 1 (30 de mayo de 2023): 21–27. http://dx.doi.org/10.11113/ijic.v13n1.384.
Texto completoPalla, Tarun Ganesh y Shahab Tayeb. "Intelligent Mirai Malware Detection for IoT Nodes". Electronics 10, n.º 11 (24 de mayo de 2021): 1241. http://dx.doi.org/10.3390/electronics10111241.
Texto completoAlruwaili, Madallah y Walaa Gouda. "Automated Breast Cancer Detection Models Based on Transfer Learning". Sensors 22, n.º 3 (24 de enero de 2022): 876. http://dx.doi.org/10.3390/s22030876.
Texto completoLiu, Yaning, Lin Han, Hexiang Wang y Bo Yin. "Classification of papillary thyroid carcinoma histological images based on deep learning". Journal of Intelligent & Fuzzy Systems 40, n.º 6 (21 de junio de 2021): 12011–21. http://dx.doi.org/10.3233/jifs-210100.
Texto completoUllah, Naeem, Ali Javed, Ali Alhazmi, Syed M. Hasnain, Ali Tahir y Rehan Ashraf. "TumorDetNet: A unified deep learning model for brain tumor detection and classification". PLOS ONE 18, n.º 9 (27 de septiembre de 2023): e0291200. http://dx.doi.org/10.1371/journal.pone.0291200.
Texto completoZawad, Syed, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian y Feng Yan. "Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 12 (18 de mayo de 2021): 10807–14. http://dx.doi.org/10.1609/aaai.v35i12.17291.
Texto completoGonzalez-Cuautle, David, Aldo Hernandez-Suarez, Gabriel Sanchez-Perez, Linda Karina Toscano-Medina, Jose Portillo-Portillo, Jesus Olivares-Mercado, Hector Manuel Perez-Meana y Ana Lucila Sandoval-Orozco. "Synthetic Minority Oversampling Technique for Optimizing Classification Tasks in Botnet and Intrusion-Detection-System Datasets". Applied Sciences 10, n.º 3 (22 de enero de 2020): 794. http://dx.doi.org/10.3390/app10030794.
Texto completoSalama, Wessam M., Moustafa H. Aly y Azza M. Elbagoury. "Lung Images Segmentation and Classification Based on Deep Learning: A New Automated CNN Approach". Journal of Physics: Conference Series 2128, n.º 1 (1 de diciembre de 2021): 012011. http://dx.doi.org/10.1088/1742-6596/2128/1/012011.
Texto completoBalasubramaniam, Sathiyabhama, Yuvarajan Velmurugan, Dhayanithi Jaganathan y Seshathiri Dhanasekaran. "A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images". Diagnostics 13, n.º 17 (24 de agosto de 2023): 2746. http://dx.doi.org/10.3390/diagnostics13172746.
Texto completoRadhi, Eman y Mohammed Kamil. "An automatic segmentation of breast ultrasound images using U-Net model". Serbian Journal of Electrical Engineering 20, n.º 2 (2023): 191–203. http://dx.doi.org/10.2298/sjee2302191r.
Texto completoKujdowicz, Monika, Dominika Januś, Anna Taczanowska-Niemczuk, Marek W. Lankosz y Dariusz Adamek. "Raman Spectroscopy as a Potential Adjunct of Thyroid Nodule Evaluation: A Systematic Review". International Journal of Molecular Sciences 24, n.º 20 (13 de octubre de 2023): 15131. http://dx.doi.org/10.3390/ijms242015131.
Texto completoAlhussainan, Norah Fahd, Belgacem Ben Youssef y Mohamed Maher Ben Ismail. "A Deep Learning Approach for Brain Tumor Firmness Detection Based on Five Different YOLO Versions: YOLOv3–YOLOv7". Computation 12, n.º 3 (1 de marzo de 2024): 44. http://dx.doi.org/10.3390/computation12030044.
Texto completoWang, Ruikui, Yuanfang Guo y Yunhong Wang. "AGS: Affordable and Generalizable Substitute Training for Transferable Adversarial Attack". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 6 (24 de marzo de 2024): 5553–62. http://dx.doi.org/10.1609/aaai.v38i6.28365.
Texto completoShah, Rajesh P., Heather M. Selby, Pritam Mukherjee, Shefali Verma, Peiyi Xie, Qinmei Xu, Millie Das, Sachin Malik, Olivier Gevaert y Sandy Napel. "Machine Learning Radiomics Model for Early Identification of Small-Cell Lung Cancer on Computed Tomography Scans". JCO Clinical Cancer Informatics, n.º 5 (junio de 2021): 746–57. http://dx.doi.org/10.1200/cci.21.00021.
Texto completoAlzubaidi, Laith, Omran Al-Shamma, Mohammed A. Fadhel, Laith Farhan, Jinglan Zhang y Ye Duan. "Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model". Electronics 9, n.º 3 (6 de marzo de 2020): 445. http://dx.doi.org/10.3390/electronics9030445.
Texto completoWildeboer, Rogier R., Christophe K. Mannaerts, Ruud J. G. van Sloun, Lars Budäus, Derya Tilki, Hessel Wijkstra, Georg Salomon y Massimo Mischi. "Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics". European Radiology 30, n.º 2 (10 de octubre de 2019): 806–15. http://dx.doi.org/10.1007/s00330-019-06436-w.
Texto completoAtarsaikhan, Gantugs, Isabel Mogollon, Katja Välimäki, Tuomas Mirtti, Teijo Pellinen y Lassi Paavolainen. "Abstract 892: Pan-cancer tumor microenvironment profiling with multiplexed immunofluorescence microscopy and self-supervised learning". Cancer Research 84, n.º 6_Supplement (22 de marzo de 2024): 892. http://dx.doi.org/10.1158/1538-7445.am2024-892.
Texto completoFeng, Liqi, Yaqin Zhao, Yichao Sun, Wenxuan Zhao y Jiaxi Tang. "Action Recognition Using a Spatial-Temporal Network for Wild Felines". Animals 11, n.º 2 (12 de febrero de 2021): 485. http://dx.doi.org/10.3390/ani11020485.
Texto completoTran-Quoc, Kim, Lieu B. Nguyen, Van Hai Luong y H. Nguyen-Xuan. "Machine learning for predicting mechanical behavior of concrete beams with 3D printed TPMS". Vietnam Journal of Mechanics 44, n.º 4 (31 de diciembre de 2022): 538–84. http://dx.doi.org/10.15625/0866-7136/17999.
Texto completoWang, Ke, Vidya Muthukumar y Christos Thrampoulidis. "Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation". IEEE Transactions on Information Theory, 2023, 1. http://dx.doi.org/10.1109/tit.2023.3320098.
Texto completoZhou, Lijia, Frederic Koehler, Danica J. Sutherland y Nathan Srebro. "Optimistic Rates: A Unifying Theory for Interpolation Learningand Regularization in Linear Regression". ACM / IMS Journal of Data Science, 16 de noviembre de 2023. http://dx.doi.org/10.1145/3594234.
Texto completoZufry, Hendra y Agus Arip Munawar. "Near-Infrared Spectroscopy for Distinguishing Malignancy in Thyroid Nodules". Applied Spectroscopy, 19 de febrero de 2024. http://dx.doi.org/10.1177/00037028241232440.
Texto completoTo, Tyrell, Tongtong Lu, Julie M. Jorns, Mollie Patton, Taly Gilat Schmidt, Tina Yen, Bing Yu y Dong Hye Ye. "Deep learning classification of deep ultraviolet fluorescence images toward intra-operative margin assessment in breast cancer". Frontiers in Oncology 13 (16 de junio de 2023). http://dx.doi.org/10.3389/fonc.2023.1179025.
Texto completoKim, Taehyun, Woonyoung Chang, Jeongyoun Ahn y Sungkyu Jung. "Double data piling: a high-dimensional solution for asymptotically perfect multi-category classification". Journal of the Korean Statistical Society, 3 de abril de 2024. http://dx.doi.org/10.1007/s42952-024-00263-6.
Texto completoFeliciani, Giacomo, Francesco Serra, Enrico Menghi, Fabio Ferroni, Anna Sarnelli, Carlo Feo, Maria Chiara Zatelli, Maria Rosaria Ambrosio, Melchiore Giganti y Aldo Carnevale. "Radiomics in the characterization of lipid-poor adrenal adenomas at unenhanced CT: time to look beyond usual density metrics". European Radiology, 11 de agosto de 2023. http://dx.doi.org/10.1007/s00330-023-10090-8.
Texto completoGiraldo‐Roldan, Daniela, Erin Crespo Cordeiro Ribeiro, Anna Luiza Damaceno Araújo, Paulo Victor Mendes Penafort, Viviane Mariano da Silva, Jeconias Câmara, Hélder Antônio Rebelo Pontes et al. "Deep learning applied to the histopathological diagnosis of ameloblastomas and ameloblastic carcinomas". Journal of Oral Pathology & Medicine, 15 de septiembre de 2023. http://dx.doi.org/10.1111/jop.13481.
Texto completoJiménez-Gaona, Yuliana, María José Rodríguez-Alvarez, Líder Escudero, Carlos Sandoval y Vasudevan Lakshminarayanan. "Ultrasound breast images denoising using generative adversarial networks (GANs)". Intelligent Data Analysis, 31 de enero de 2024, 1–18. http://dx.doi.org/10.3233/ida-230631.
Texto completoYang, Fan, Yujie Li, Xiaolu Li, Xiaoduo Yu, Yanfeng Zhao, Lin Li, Lizhi Xie y Meng Lin. "The utility of texture analysis based on quantitative synthetic magnetic resonance imaging in nasopharyngeal carcinoma: a preliminary study". BMC Medical Imaging 23, n.º 1 (25 de enero de 2023). http://dx.doi.org/10.1186/s12880-023-00968-w.
Texto completoMontaha, Sidratul, Sami Azam, Md Rahad Islam Bhuiyan, Sadia Sultana Chowa, Md Saddam Hossain Mukta y Mirjam Jonkman. "Malignancy pattern analysis of breast ultrasound images using clinical features and a graph convolutional network". DIGITAL HEALTH 10 (enero de 2024). http://dx.doi.org/10.1177/20552076241251660.
Texto completoSinghal, Aneesh B., Oguzhan Kursun, Mehmet A. Topcuoglu, Joshua Fok, Bruce Barton y Susanne Muehlschlegel. "Abstract WP431: Distinguishing RCVS-associated Subarachnoid Hemorrhage From Cryptogenic and Aneurysmal Subarachnoid Hemorrhage". Stroke 44, suppl_1 (febrero de 2013). http://dx.doi.org/10.1161/str.44.suppl_1.awp431.
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