Relevant bibliographies by topics / Implicit regularization

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Implicit regularization'

Author: Grafiati
Published: 30 November 2024
Last updated: 31 July 2025

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Journal articles on the topic "Implicit regularization"

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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.

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We first introduce an implicit relaxed method with regularization for finding a common element of the set of fixed points of an asymptotically strict pseudocontractive mappingSin the intermediate sense and the set of solutions of the minimization problem (MP) for a convex and continuously Frechet differentiable functional in the setting of Hilbert spaces. The implicit relaxed method with regularization is based on three well-known methods: the extragradient method, viscosity approximation method, and gradient projection algorithm with regularization. We derive a weak convergence theorem for tw
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Klädtke, Manuel, and Moritz Schulze Darup. "Towards explainable data-driven predictive control with regularizations." at - Automatisierungstechnik 73, no. 6 (2025): 365–82. https://doi.org/10.1515/auto-2024-0161.

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Abstract Data-driven predictive control (DPC), using linear combinations of recorded trajectory data, has recently emerged as a popular alternative to traditional model predictive control (MPC). Without an explicitly enforced prediction model, the effects of commonly used regularization terms – and the resulting predictions – can be opaque. This opacity may lead to practical challenges, such as reliance on empirical tuning of regularization parameters based on closed-loop performance, and potentially misleading heuristic interpretations of norm-based regularizations. However, by examining the
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FARGNOLI, H. G., A. P. BAÊTA SCARPELLI, L. C. T. BRITO, et al. "ULTRAVIOLET AND INFRARED DIVERGENCES IN IMPLICIT REGULARIZATION: A CONSISTENT APPROACH." Modern Physics Letters A 26, no. 04 (2011): 289–302. http://dx.doi.org/10.1142/s0217732311034773.

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Implicit Regularization is a four-dimensional regularization initially conceived to treat ultraviolet divergences. It has been successfully tested in several instances in the literature, more specifically in those where Dimensional Regularization does not apply. In the present contribution, we extend the method to handle infrared divergences as well. We show that the essential steps which rendered Implicit Regularization adequate in the case of ultraviolet divergences have their counterpart for infrared ones. Moreover, we show that a new scale appears, typically an infrared scale which is comp
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Sampaio, 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 (2006): 436–57. http://dx.doi.org/10.1007/s10773-006-9045-z.

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Al-Tam, Faroq, António dos Anjos, and Hamid Reza Shahbazkia. "Iterative illumination correction with implicit regularization." Signal, Image and Video Processing 10, no. 5 (2015): 967–74. http://dx.doi.org/10.1007/s11760-015-0847-4.

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Dandi, 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 (2022): 6454–62. http://dx.doi.org/10.1609/aaai.v36i6.20597.

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A major obstacle to achieving global convergence in distributed and federated learning is the misalignment of gradients across clients or mini-batches due to heterogeneity and stochasticity of the distributed data. In this work, we show that data heterogeneity can in fact be exploited to improve generalization performance through implicit regularization. One way to alleviate the effects of heterogeneity is to encourage the alignment of gradients across different clients throughout training. Our analysis reveals that this goal can be accomplished by utilizing the right optimization method that
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Rosado, 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.

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Liu, 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.

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Li, Zhemin, Tao Sun, Hongxia Wang, and Bao Wang. "Adaptive and Implicit Regularization for Matrix Completion." SIAM Journal on Imaging Sciences 15, no. 4 (2022): 2000–2022. http://dx.doi.org/10.1137/22m1489228.

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Belytschko, 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.

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Dissertations / Theses on the topic "Implicit regularization"

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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.

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The development of numerical methods for the incompressible Navier-Stokes equations received much attention in the past 50 years. Finite element methods emerged given their robustness and reliability. In our work, we choose the P2-P1 finite element for space approximation which gives 2nd-order accuracy for velocity and 1st-order accuracy for pressure. Our research focuses on the development of several high-order semi-implicit time-stepping methods to compute unsteady flows. The methods investigated include backward difference formulae (SBDF) and defect correction strategy (DC). Using the defec
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Ayme, Alexis. "Supervised learning with missing data : a non-asymptotic point of view." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS252.

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Les valeurs manquantes sont courantes dans la plupart des ensembles de données du monde réel, en raison de la combinaison de sources multiples et d'informations intrinsèquement manquantes, telles que des défaillances de capteurs ou des questions d'enquête sans réponse. La présence de valeurs manquantes empêche souvent l'application d'algorithmes d'apprentissage standard. Cette thèse examinevaleurs manquantes dans un contexte de prédiction, visant à obtenir des prédictions précises malgré l'occurrence de données manquantes dans les données d'apprentissage et de test. L'objectif de cette thèse e
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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.

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La détermination de la forme d'un obstacle élastique immergé dans un milieu fluide à partir de mesures du champ d'onde diffracté est un problème d'un vif intérêt dans de nombreux domaines tels que le sonar, l'exploration géophysique et l'imagerie médicale. A cause de son caractère non-linéaire et mal posé, ce problème inverse de l'obstacle (IOP) est très difficile à résoudre, particulièrement d'un point de vue numérique. De plus, son étude requiert la compréhension de la théorie du problème de diffraction direct (DP) associé, et la maîtrise des méthodes de résolution correspondantes. Le travai
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Pereira, Ana Isabel Costa. "Implicit Regularization in a QCD decay of the Higgs boson." Master's thesis, 2021. http://hdl.handle.net/10316/98040.

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Dissertação de Mestrado em Física apresentada à Faculdade de Ciências e Tecnologia<br>O 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 ob
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Book chapters on the topic "Implicit regularization"

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He, Fengxiang, and Dacheng Tao. "Stochastic Gradient Descent as an Implicit Regularization." In Machine Learning: Foundations, Methodologies, and Applications. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-16-8233-9_6.

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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. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11567646_29.

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Wahba, G. "Regularization and Cross Validation Methods for Nonlinear, Implicit, Ill-posed Inverse Problems." In Geophysical Data Inversion Methods and Applications. Vieweg+Teubner Verlag, 1990. http://dx.doi.org/10.1007/978-3-322-89416-8_1.

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Zavarise, 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. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17484-1_3.

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Usenov, Izat. "Combined Regularization Method for Solving an Implicit Operator Equation of the First Kind." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64010-0_3.

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Menini, 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. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33415-3_33.

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Spieker, Veronika, Hannah Eichhorn, Jonathan K. Stelter, et al. "Self-supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representations." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72104-5_59.

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Ehrhardt, Jan, and Heinz Handels. "Implicitly Solved Regularization for Learning-Based Image Registration." In Machine Learning in Medical Imaging. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45673-2_14.

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Castillo, Myrna, Mahtab Dahaghin, Matteo Toso, and Alessio Del Bue. "Contrastive Gaussian Clustering for Weakly Supervised 3D Scene Segmentation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-78347-0_8.

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Abstract3D scene segmentation is a crucial task in Computer Vision, with applications in autonomous driving, augmented reality, and robotics. Traditional methods often struggle to provide consistent and accurate segmentation across different viewpoints. To address this, we look at the growing field of novel view synthesis. Methods like NeRF and 3DGS take a set of images and implicitly learn a multi-view consistent representation of the geometry of the scene; the same strategy can be extended to learn a 3D segmentation of the scene that is consistent with the 2D segmentation of an initial train
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Banerjee, 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.

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Recovering a physics-driven model, i.e. a governing set of equations of the underlying dynamical systems, from the real-world data has been of recent interest. Most existing methods either operate on simulation data with unrealistically high sampling rates or require explicit measurements of all system variables, which is not amenable in real-world deployments. Moreover, they assume the timestamps of external perturbations to the physical system are known a priori, without uncertainty, implicitly discounting any sensor time-synchronization or human reporting errors. In this paper, we propose a
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Conference papers on the topic "Implicit regularization"

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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). IEEE, 2024. http://dx.doi.org/10.1109/isit57864.2024.10619577.

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Ivinskij, Vadimas, and Inga Morkvėnaitė-Vilkončienė. "Polynomial Approximation Degree Influence on Implicit Network Regularization for Impedance Signal Reconstruction." In 2025 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream). IEEE, 2025. https://doi.org/10.1109/estream66938.2025.11016831.

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Liu, Xuan, Yutong He, Yaoqin Xie, Shan Tan, and Xiaokun Liang. "A CT metal artifact reduction method driven by implicit neural representation and dual-domain regularization." In Physics of Medical Imaging, edited by John M. Sabol, Shiva Abbaszadeh, and Ke Li. SPIE, 2025. https://doi.org/10.1117/12.3046430.

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Shenvi, Riya. "Physics-Informed Neural Networks for Approximating Loss Evolution of an Artificial Neural Network: Novel Approach to Implicit Regularization." In 2024 IEEE MIT Undergraduate Research Technology Conference (URTC). IEEE, 2024. https://doi.org/10.1109/urtc65039.2024.10937527.

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Shen, Xin, Jie Feng, Ruimin Feng, Yuyao Zhang, and Hongjiang Wei. "IMJ-PLUS: Implicit Representation for Dynamic MRI and Coil Sensitivity Joint Reconstruction Using Low-Rank PLUS Sparse Regularization." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10980969.

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Gunasekar, 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.

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Milanesi, 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.

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Dupe, 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.

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Yao, 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.

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Huang, 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.

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