Academic literature on the topic 'Implicit regularization'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Implicit regularization.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Implicit regularization"
Ceng, Lu-Chuan, Qamrul Hasan Ansari, and Ching-Feng Wen. "Implicit Relaxed and Hybrid Methods with Regularization for Minimization Problems and Asymptotically Strict Pseudocontractive Mappings in the Intermediate Sense." Abstract and Applied Analysis 2013 (2013): 1–14. http://dx.doi.org/10.1155/2013/854297.
Full textFARGNOLI, H. G., A. P. BAÊTA SCARPELLI, L. C. T. BRITO, B. HILLER, MARCOS SAMPAIO, M. C. NEMES, and A. A. OSIPOV. "ULTRAVIOLET AND INFRARED DIVERGENCES IN IMPLICIT REGULARIZATION: A CONSISTENT APPROACH." Modern Physics Letters A 26, no. 04 (February 10, 2011): 289–302. http://dx.doi.org/10.1142/s0217732311034773.
Full textSampaio, Marcos, A. P. Baêta Scarpelli, J. E. Ottoni, and M. C. Nemes. "Implicit Regularization and Renormalization of QCD." International Journal of Theoretical Physics 45, no. 2 (February 2006): 436–57. http://dx.doi.org/10.1007/s10773-006-9045-z.
Full textAl-Tam, Faroq, António dos Anjos, and Hamid Reza Shahbazkia. "Iterative illumination correction with implicit regularization." Signal, Image and Video Processing 10, no. 5 (December 11, 2015): 967–74. http://dx.doi.org/10.1007/s11760-015-0847-4.
Full textDandi, Yatin, Luis Barba, and Martin Jaggi. "Implicit Gradient Alignment in Distributed and Federated Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6454–62. http://dx.doi.org/10.1609/aaai.v36i6.20597.
Full textLin, Huangxing, Yihong Zhuang, Xinghao Ding, Delu Zeng, Yue Huang, Xiaotong Tu, and John Paisley. "Self-Supervised Image Denoising Using Implicit Deep Denoiser Prior." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (June 26, 2023): 1586–94. http://dx.doi.org/10.1609/aaai.v37i2.25245.
Full textLiu, Yuan, Yanzhi Song, Zhouwang Yang, and Jiansong Deng. "Implicit surface reconstruction with total variation regularization." Computer Aided Geometric Design 52-53 (March 2017): 135–53. http://dx.doi.org/10.1016/j.cagd.2017.02.005.
Full textLi, Zhemin, Tao Sun, Hongxia Wang, and Bao Wang. "Adaptive and Implicit Regularization for Matrix Completion." SIAM Journal on Imaging Sciences 15, no. 4 (November 22, 2022): 2000–2022. http://dx.doi.org/10.1137/22m1489228.
Full textBelytschko, T., S. P. Xiao, and C. Parimi. "Topology optimization with implicit functions and regularization." International Journal for Numerical Methods in Engineering 57, no. 8 (2003): 1177–96. http://dx.doi.org/10.1002/nme.824.
Full textRosado, R. J. C., A. Cherchiglia, M. Sampaio, and B. Hiller. "An Implicit Regularization Approach to Chiral Models." Acta Physica Polonica B Proceedings Supplement 17, no. 6 (2024): 1. http://dx.doi.org/10.5506/aphyspolbsupp.17.6-a15.
Full textDissertations / Theses on the topic "Implicit regularization"
Loy, Kak Choon. "Efficient Semi-Implicit Time-Stepping Schemes for Incompressible Flows." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36442.
Full textAyme, Alexis. "Supervised learning with missing data : a non-asymptotic point of view." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS252.
Full textMissing values are common in most real-world data sets due to the combination of multiple sources andinherently missing information, such as sensor failures or unanswered survey questions. The presenceof missing values often prevents the application of standard learning algorithms. This thesis examinesmissing values in a prediction context, aiming to achieve accurate predictions despite the occurrence ofmissing data in both training and test datasets. The focus of this thesis is to theoretically analyze specific algorithms to obtain finite-sample guarantees. We derive minimax lower bounds on the excess risk of linear predictions in presence of missing values.Such lower bounds depend on the distribution of the missing pattern, and can grow exponentially withthe dimension. We propose a very simple method consisting in applying Least-Square procedure onthe most frequent missing patterns only. Such a simple method turns out to be near minimax-optimalprocedure, which departs from the Least-Square algorithm applied to all missing patterns. Followingthis, we explore the impute-then-regress method, where imputation is performed using the naive zeroimputation, and the regression step is carried out via linear models, whose parameters are learned viastochastic gradient descent. We demonstrate that this very simple method offers strong finite-sampleguarantees in high-dimensional settings. Specifically, we show that the bias of this method is lowerthan the bias of ridge regression. As ridge regression is often used in high dimensions, this proves thatthe bias of missing data (via zero imputation) is negligible in some high-dimensional settings. Thesefindings are illustrated using random features models, which help us to precisely understand the role ofdimensionality. Finally, we study different algorithm to handle linear classification in presence of missingdata (logistic regression, perceptron, LDA). We prove that LDA is the only model that can be valid forboth complete and missing data for some generic settings
Estecahandy, Elodie. "Contribution à l'analyse mathématique et à la résolution numérique d'un problème inverse de scattering élasto-acoustique." Phd thesis, Université de Pau et des Pays de l'Adour, 2013. http://tel.archives-ouvertes.fr/tel-00880628.
Full textPereira, Ana Isabel Costa. "Implicit Regularization in a QCD decay of the Higgs boson." Master's thesis, 2021. http://hdl.handle.net/10316/98040.
Full textO regime perturbativo de Cromodinâmica Quântica envolve o aparecimento de divergências nas amplitudes de um processo. No entanto, as observáveis físicas devem ser finitas e, portanto, todas as divergências que surgem devem ser canceladas. De acordo com o teorema KLN, as divergências infravermelhas que aparecem numa taxa de decaimento ou secção eficaz em QCD devem cancelar-se ao juntar as contribuições das partes virtual e real que contribuem para a mesma ordem em teoria de perturbações. Neste trabalho, o objetivo principal é calcular a taxa de decaimento do bosão de Higgs em gluões modelado por um Lagrangiano efetivo no limite da massa do quark top infinita, e verificar o cancelamento das divergências. Para tal, derivamos as regras de Feynman do Lagrangiano efetivo para descrever a interação entre os gluões e o bosão de Higgs e estas são usadas para construir as amplitudes dos diagramas virtuais e reais do processo. Em seguida, usamos a IReg, que é um esquema de regularização não dimensional que trabalha na dimensão física da teoria e permite a separação das divergências de ultravioleta e infravermelhas de uma amplitude. Os integrais divergentes de ultravioleta são escritos como integrais divergentes básicos e os integrais finitos são avaliados usando o software Mathematica. Em seguida, usamos esses integrais para calcular a taxa de decaimento virtual do processo como uma correção à taxa de decaimento a nível árvore. Introduzimos o formalismo de spin-helicidade para calcular a amplitude real. Em seguida, estudamos o cálculo explícito do espaço fase do decaimento real e integramos a amplitude real ao longo das variáveis de integração do espaço fase para obter o decaimento real. Por fim, somamos as contribuições das taxas de decaimento virtual e real para obter o resultado final, que reproduz resultados conhecidos da literatura.
Perturbative Quantum Chromodynamics involves the appearance of divergences in the amplitudes of a process. However, physical observables must befinite and therefore, all the divergences that emerge must be cancelled. The KLN theorem states that the infrared divergences that appear in a QCD decay rate or cross section must cancel when putting together the contributions from the virtual and real parts that contribute at the same order in perturbation theory. In this work, the main goal is to calculate the decay rate of the QCD decay of the Higgs boson into gluons modeled by an effective Lagrangian in the limit of infinite top quark mass and verify the KLN theorem, using the Implicit Regularization (IReg) as opposed to Dimensional Regularization. We derive the Feynman rules of the effective Lagrangian to describe the interaction between gluons and the Higgs boson and use them to construct the amplitudes of the process’ virtual and real diagrams.We then use IReg, which is a non-dimensional regularization scheme that works in the physical dimension of the theory and allows for the separation of the ultraviolet and infrared divergences of an amplitude. The ultraviolet divergent integrals are written as basic divergent integrals and the finite integrals are evaluated using the software Mathematica. We then use these integrals to compute the virtual decay rate of the process as a correction to the tree-level decay rate. We introduce the spin-helicity formalism to compute the real amplitude. We then study the explicit computation of the phase space of the real decay and integrate the squared real amplitude over the phase space to obtain the real decay. At last, we add the contributions from both virtual and real decay rates to obtain the final result which is finite as expected, reproducing known results in the literature.
Outro - CERN/FIS-PAR/0040/2019
Book chapters on the topic "Implicit regularization"
Shafrir, David, Nir A. Sochen, and Rachid Deriche. "Regularization of Mappings Between Implicit Manifolds of Arbitrary Dimension and Codimension." In Lecture Notes in Computer Science, 344–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11567646_29.
Full textWahba, G. "Regularization and Cross Validation Methods for Nonlinear, Implicit, Ill-posed Inverse Problems." In Geophysical Data Inversion Methods and Applications, 3–13. Wiesbaden: Vieweg+Teubner Verlag, 1990. http://dx.doi.org/10.1007/978-3-322-89416-8_1.
Full textZavarise, Giorgio, Laura De Lorenzis, and Robert L. Taylor. "On Regularization of the Convergence Path for the Implicit Solution of Contact Problems." In Recent Developments and Innovative Applications in Computational Mechanics, 17–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17484-1_3.
Full textUsenov, Izat. "Combined Regularization Method for Solving an Implicit Operator Equation of the First Kind." In Lecture Notes in Networks and Systems, 24–33. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64010-0_3.
Full textMenini, Anne, Pierre-André Vuissoz, Jacques Felblinger, and Freddy Odille. "Joint Reconstruction of Image and Motion in MRI: Implicit Regularization Using an Adaptive 3D Mesh." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, 264–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33415-3_33.
Full textSpieker, Veronika, Hannah Eichhorn, Jonathan K. Stelter, Wenqi Huang, Rickmer F. Braren, Daniel Rueckert, Francisco Sahli Costabal, et al. "Self-supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representations." In Lecture Notes in Computer Science, 614–24. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72104-5_59.
Full textEhrhardt, Jan, and Heinz Handels. "Implicitly Solved Regularization for Learning-Based Image Registration." In Machine Learning in Medical Imaging, 137–46. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45673-2_14.
Full textBanerjee, Ayan, and Sandeep K. S. Gupta. "Recovering Implicit Physics Model Under Real-World Constraints." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240556.
Full textXiao, Jinying, Ping Li, and Jie Nie. "TED: Accelerate Model Training by Internal Generalization." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240823.
Full textThomas, Dominic. "Les Sans-papiers." In Postcolonial Realms of Memory, 255–66. Liverpool University Press, 2020. http://dx.doi.org/10.3828/liverpool/9781789620665.003.0024.
Full textConference papers on the topic "Implicit regularization"
Xu, Qunzhi, Yi Yu, and Yajun Mei. "Quickest Detection in High-Dimensional Linear Regression Models via Implicit Regularization." In 2024 IEEE International Symposium on Information Theory (ISIT), 1059–64. IEEE, 2024. http://dx.doi.org/10.1109/isit57864.2024.10619577.
Full textGunasekar, Suriya, Blake Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, and Nathan Srebro. "Implicit Regularization in Matrix Factorization." In 2018 Information Theory and Applications Workshop (ITA). IEEE, 2018. http://dx.doi.org/10.1109/ita.2018.8503198.
Full textMilanesi, Paolo, Hachem Kadri, Stephane Ayache, and Thierry Artieres. "Implicit Regularization in Deep Tensor Factorization." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533690.
Full textDupe, Francois Xavier, Sebastien Bougleux, Luc Brun, Olivier Lezoray, and Abderahim Elmoataz. "Kernel-Based Implicit Regularization of Structured Objects." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.525.
Full textYao, Tianyi, Daniel LeJeune, Hamid Javadi, Richard G. Baraniuk, and Genevera I. Allen. "Minipatch Learning as Implicit Ridge-Like Regularization." In 2021 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2021. http://dx.doi.org/10.1109/bigcomp51126.2021.00021.
Full textHuang, Xiaoyang, Yi Zhang, Kai Chen, Teng Li, Wenjun Zhang, and Bingbing Ni. "Learning Shape Primitives via Implicit Convexity Regularization." In 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2023. http://dx.doi.org/10.1109/iccv51070.2023.00337.
Full textCherchiglia, A. "Systematizing Implicit Regularization for Multi-Loop Feynman Diagrams." In 4th International Conference on Fundamental Interactions. Trieste, Italy: Sissa Medialab, 2011. http://dx.doi.org/10.22323/1.124.0016.
Full textCataltepe, Zehra, Mahiye Uluyagmur, and Esengul Tayfur. "TV program recommendation using implicit feedback with adaptive regularization." In 2012 20th Signal Processing and Communications Applications Conference (SIU). IEEE, 2012. http://dx.doi.org/10.1109/siu.2012.6204780.
Full textKumar, Akshay, Akshay Malhotra, and Shahab Hamidi-Rad. "Group Sparsity via Implicit Regularization for MIMO Channel Estimation." In 2023 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2023. http://dx.doi.org/10.1109/wcnc55385.2023.10118737.
Full textYao, Lina, Xianzhi Wang, Quan Z. Sheng, Wenjie Ruan, and Wei Zhang. "Service Recommendation for Mashup Composition with Implicit Correlation Regularization." In 2015 IEEE International Conference on Web Services (ICWS). IEEE, 2015. http://dx.doi.org/10.1109/icws.2015.38.
Full text