Gotowa bibliografia na temat „Benign overfitting”
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Artykuły w czasopismach na temat "Benign overfitting"
Bartlett, Peter L., Philip M. Long, Gábor Lugosi i 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.
Pełny tekst źródłaPeters, Evan, i 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.
Pełny tekst źródłaBartlett, Peter L., Andrea Montanari i Alexander Rakhlin. "Deep learning: a statistical viewpoint". Acta Numerica 30 (maj 2021): 87–201. http://dx.doi.org/10.1017/s0962492921000027.
Pełny tekst źródłaWang, Ke, i 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 (marzec 2022): 260–84. http://dx.doi.org/10.1137/21m1415121.
Pełny tekst źródłaHu, 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.
Pełny tekst źródłaMontaha, Sidratul, Sami Azam, A. K. M. Rakibul Haque Rafid, Sayma Islam, Pronab Ghosh i 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 (4.08.2022): e0269826. http://dx.doi.org/10.1371/journal.pone.0269826.
Pełny tekst źródłaWindisch, Paul, Carole Koechli, Susanne Rogers, Christina Schröder, Robert Förster, Daniel R. Zwahlen i 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.
Pełny tekst źródłaLiang, ShuFen, HuiLin Liu, FangChen Yang, Chuanbo Qin i 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 (1.08.2021): 2117–23. http://dx.doi.org/10.1166/jmihi.2021.3448.
Pełny tekst źródłaLiu, Xinwei, Xiaojun Jia, Jindong Gu, Yuan Xun, Siyuan Liang i 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.
Pełny tekst źródłaDoimo, Diego, Aldo Glielmo, Sebastian Goldt i Alessandro Laio. "Redundant representations help generalization in wide neural networks * , †". Journal of Statistical Mechanics: Theory and Experiment 2023, nr 11 (1.11.2023): 114011. http://dx.doi.org/10.1088/1742-5468/aceb4f.
Pełny tekst źródłaRozprawy doktorskie na temat "Benign overfitting"
Sigalla, Suzanne. "Contributions to structured high-dimensional inference". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAG013.
Pełny tekst źródłaIn 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
Streszczenia konferencji na temat "Benign overfitting"
Wang, Ke, i Christos Thrampoulidis. "Benign Overfitting in Binary Classification of Gaussian Mixtures". W ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9413946.
Pełny tekst źródłaChretien, Stephane, i Emmanuel Caron-Parte. "Benign overfitting of fully connected Deep Nets:A Sobolev space viewpoint". W 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.
Pełny tekst źródłaChinthapally, Srinivas, Sidhardha Nuli, Arnab Das i Akshay Hedaoo. "Method to Backout Load From Strain Gauges Using Machine Learning". W ASME 2023 Gas Turbine India Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/gtindia2023-118279.
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