Auswahl der wissenschaftlichen Literatur zum Thema „Benign overfitting“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Inhaltsverzeichnis
Machen Sie sich mit den Listen der aktuellen Artikel, Bücher, Dissertationen, Berichten und anderer wissenschaftlichen Quellen zum Thema "Benign overfitting" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Zeitschriftenartikel zum Thema "Benign overfitting"
Bartlett, Peter L., Philip M. Long, Gábor Lugosi und Alexander Tsigler. „Benign overfitting in linear regression“. Proceedings of the National Academy of Sciences 117, Nr. 48 (24.04.2020): 30063–70. http://dx.doi.org/10.1073/pnas.1907378117.
Der volle Inhalt der QuellePeters, Evan, und Maria Schuld. „Generalization despite overfitting in quantum machine learning models“. Quantum 7 (20.12.2023): 1210. http://dx.doi.org/10.22331/q-2023-12-20-1210.
Der volle Inhalt der QuelleBartlett, Peter L., Andrea Montanari und Alexander Rakhlin. „Deep learning: a statistical viewpoint“. Acta Numerica 30 (Mai 2021): 87–201. http://dx.doi.org/10.1017/s0962492921000027.
Der volle Inhalt der QuelleWang, Ke, und Christos Thrampoulidis. „Binary Classification of Gaussian Mixtures: Abundance of Support Vectors, Benign Overfitting, and Regularization“. SIAM Journal on Mathematics of Data Science 4, Nr. 1 (März 2022): 260–84. http://dx.doi.org/10.1137/21m1415121.
Der volle Inhalt der QuelleHu, Wei. „Understanding Surprising Generalization Phenomena in Deep Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 20 (24.03.2024): 22669. http://dx.doi.org/10.1609/aaai.v38i20.30285.
Der volle Inhalt der QuelleMontaha, Sidratul, Sami Azam, A. K. M. Rakibul Haque Rafid, Sayma Islam, Pronab Ghosh und Mirjam Jonkman. „A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity“. PLOS ONE 17, Nr. 8 (04.08.2022): e0269826. http://dx.doi.org/10.1371/journal.pone.0269826.
Der volle Inhalt der QuelleWindisch, Paul, Carole Koechli, Susanne Rogers, Christina Schröder, Robert Förster, Daniel R. Zwahlen und Stephan Bodis. „Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review“. Cancers 14, Nr. 11 (27.05.2022): 2676. http://dx.doi.org/10.3390/cancers14112676.
Der volle Inhalt der QuelleLiang, ShuFen, HuiLin Liu, FangChen Yang, Chuanbo Qin und Yue Feng. „Classification of Benign and Malignant Pulmonary Nodules Using a Regularized Extreme Learning Machine“. Journal of Medical Imaging and Health Informatics 11, Nr. 8 (01.08.2021): 2117–23. http://dx.doi.org/10.1166/jmihi.2021.3448.
Der volle Inhalt der QuelleLiu, Xinwei, Xiaojun Jia, Jindong Gu, Yuan Xun, Siyuan Liang und Xiaochun Cao. „Does Few-Shot Learning Suffer from Backdoor Attacks?“ Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 18 (24.03.2024): 19893–901. http://dx.doi.org/10.1609/aaai.v38i18.29965.
Der volle Inhalt der QuelleDoimo, Diego, Aldo Glielmo, Sebastian Goldt und Alessandro Laio. „Redundant representations help generalization in wide neural networks * , †“. Journal of Statistical Mechanics: Theory and Experiment 2023, Nr. 11 (01.11.2023): 114011. http://dx.doi.org/10.1088/1742-5468/aceb4f.
Der volle Inhalt der QuelleDissertationen zum Thema "Benign overfitting"
Sigalla, Suzanne. „Contributions to structured high-dimensional inference“. Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAG013.
Der volle Inhalt der QuelleIn this thesis, we consider the three following problems: clustering in Bipartite Stochastic Block Model, estimation of topic-document matrix in topic model, and benign overfitting in nonparametric regression. First, we consider the graph clustering problem in the Bipartite Stochastic Block Model (BSBM). The BSBM is a non-symmetric generalization of the Stochastic Block Model, with two sets of vertices. We provide an algorithm called the Hollowed Lloyd's algorithm, which allows one to classify vertices of the smallest set with high probability. We provide statistical guarantees on this algorithm, which is computationnally fast and simple to implement. We establish a sufficient condition for clustering in BSBM. Our results improve on previous works on BSBM, in particular in the high-dimensional regime. Second, we study the problem of assigning topics to documents using topic models. Topic models allow one to discover hidden structures in a large corpus of documents through dimension reduction. Each topic is considered as a probability distribution on the dictionary of words, and each document is considered as a mixture of topics. We introduce an algotihm called the Successive Projection Overlapping Clustering (SPOC) algorithm, inspired by the Successive Projection Algorithm for Non-negative Matrix Factorization. The SPOC algorithm is computationnally fast and simple to implement. We provide statistical guarantees on the outcome of the algorithm. In particular, we provide near matching minimax upper and lower bounds on its estimation risk under the Frobenius and the l1-norm. Our clustering procedure is adaptive in the number of topics. Finally, the third problem we study is a nonparametric regression problem. We consider local polynomial estimators with singular kernel, which we prove to be minimax optimal, adaptive to unknown smoothness, and interpolating with high probability. This property is called benign overfitting
Konferenzberichte zum Thema "Benign overfitting"
Wang, Ke, und Christos Thrampoulidis. „Benign Overfitting in Binary Classification of Gaussian Mixtures“. In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9413946.
Der volle Inhalt der QuelleChretien, Stephane, und Emmanuel Caron-Parte. „Benign overfitting of fully connected Deep Nets:A Sobolev space viewpoint“. In ESANN 2021 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2021. http://dx.doi.org/10.14428/esann/2021.es2021-37.
Der volle Inhalt der QuelleChinthapally, Srinivas, Sidhardha Nuli, Arnab Das und Akshay Hedaoo. „Method to Backout Load From Strain Gauges Using Machine Learning“. In ASME 2023 Gas Turbine India Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/gtindia2023-118279.
Der volle Inhalt der Quelle