Literatura científica selecionada sobre o tema "L0 regularization"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Consulte a lista de atuais artigos, livros, teses, anais de congressos e outras fontes científicas relevantes para o tema "L0 regularization".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Artigos de revistas sobre o assunto "L0 regularization"
Zhu, Jiehua, e Xiezhang Li. "A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed Tomography". Journal of Applied Mathematics 2019 (2 de junho de 2019): 1–8. http://dx.doi.org/10.1155/2019/8398035.
Texto completo da fonteLi, Xiezhang, Guocan Feng e Jiehua Zhu. "An Algorithm of l1-Norm and l0-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection". International Journal of Biomedical Imaging 2020 (28 de agosto de 2020): 1–6. http://dx.doi.org/10.1155/2020/8873865.
Texto completo da fonteFan, Qinwei, e Ting Liu. "Smoothing L0 Regularization for Extreme Learning Machine". Mathematical Problems in Engineering 2020 (6 de julho de 2020): 1–10. http://dx.doi.org/10.1155/2020/9175106.
Texto completo da fonteLee, Kyung-Sik. "Signomial Classification Method with0-regularization". IE interfaces 24, n.º 2 (1 de junho de 2011): 151–55. http://dx.doi.org/10.7232/ieif.2011.24.2.151.
Texto completo da fonteZhou, Xiaoqing, Rongrong Hou e Yuhan Wu. "Structural damage detection based on iteratively reweighted l1 regularization algorithm". Advances in Structural Engineering 22, n.º 6 (7 de dezembro de 2018): 1479–87. http://dx.doi.org/10.1177/1369433218817138.
Texto completo da fonteLi, Kun, Na Qi e Qing Zhu. "Fluid Simulation with an L0 Based Optical Flow Deformation". Applied Sciences 10, n.º 18 (12 de setembro de 2020): 6351. http://dx.doi.org/10.3390/app10186351.
Texto completo da fonteFrommlet, Florian, e Grégory Nuel. "An Adaptive Ridge Procedure for L0 Regularization". PLOS ONE 11, n.º 2 (5 de fevereiro de 2016): e0148620. http://dx.doi.org/10.1371/journal.pone.0148620.
Texto completo da fonteZhang, Lingli, e An Luo. "l1/2 regularization for wavelet frames based few-view CT reconstruction". E3S Web of Conferences 269 (2021): 01020. http://dx.doi.org/10.1051/e3sconf/202126901020.
Texto completo da fonteWang, Guodong. "Image Decomposition Model OSV with L0 Sparse Regularization". Journal of Information and Computational Science 12, n.º 2 (20 de janeiro de 2015): 743–50. http://dx.doi.org/10.12733/jics20105230.
Texto completo da fonteChristou, Antonis, e Andreas Artemiou. "Adaptive L0 Regularization for Sparse Support Vector Regression". Mathematics 11, n.º 13 (22 de junho de 2023): 2808. http://dx.doi.org/10.3390/math11132808.
Texto completo da fonteTeses / dissertações sobre o assunto "L0 regularization"
Bechensteen, Arne. "Optimisation L2-L0 contrainte et application à la microscopie à molécule unique". Thesis, Université Côte d'Azur, 2020. http://www.theses.fr/2020COAZ4068.
Texto completo da fonteSparse optimization is crucial in today's society, as this is used in multiple domains, such as denoising, compression, machine learning, and variable selection. Sparse optimization is also vital in single-molecule localization microscopy, a microscopy method widely used in biology. However, obtaining a good sparse solution of a signal is computationally challenging. This thesis focuses on sparse optimization in the form of minimizing the least square loss function under a k-sparse constraint with an L0 pseudo-norm (the constrained L2-L0 problem). We also study the sum of the least square loss function and an L0 penalty term (the penalized L2-L0 problem). Both problems are non-convex, non-continuous, and NP-hard. We propose three new approaches to sparse optimization. We present first a continuous relaxation of the constrained problem and present a method to minimize the proposed relaxation. Secondly, we reformulate the L0 pseudo-norm as a convex minimization problem. This is done by introducing an auxiliary variable, and we present an exact biconvex reformulation of the constrained (CoBic) and penalized (PeBic) problems. Finally, we present a method to minimize the product of the data fidelity term and the regularization term. The latter is still an ongoing research work. We apply the three proposed methods (relaxation, CoBic, and PeBic) to single-molecule localization microscopy and compare them with other commonly used algorithms in sparse optimization. The proposed algorithms' results are as good as the state-of-the-art in grid-based methods. Furthermore, fixing the sparsity constraint constant is usually more intuitive than fixing the penalty parameter, making the constraint approach attractive for applications
Delmer, Alice. "Goniométrie parcimonieuse de sources radioélectriques : modèles, algorithmes et mises en œuvre robustes". Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG085.
Texto completo da fonteThis thesis deals with the direction-of-arrival (DOA) estimation of non-cooperative radio transmitters from signals received on an antenna array. The applications targeted in this work are the airborne scenario, characterized by a number of sources higher than the number of sensors, and the urban environment scenario, characterized by coherent multipath.Conventional direction-of-arrival estimation methods such as beamforming and Capon method or high resolution methods such as MUSIC are not efficient in such scenarios. The maximum likelihood method suffers from a computational complexity incompatible with current operational systems.In order to overcome these limitations, the problem of direction-of-arrival estimation is treated here with a sparse formalism, perfectly adapted to the use of calibration tables in operational systems. After having shown the interests of an approach based on a regularization by the L0 norm, this thesis tackles the technical issues that are the regularization parameter and the global convergence of optimization algorithms. To this end, we construct and statistically study sparse representations adapted to i) airborne scenarios, and ii) urban environments. The equivalence with the maximum likelihood given by a constrained formulation then allows us to determine a theoretical admissible interval for the regularization parameter. We also study the minimizers and error surfaces of different optimization criteria. This allows us to propose iterative minimization schemes that increase the probability of global convergence and thus are less sensitive to initialization. In that respect, the proposed ALICE-L0 algorithm enables to separate close sources
Arceci, Francesca. "Variational algorithms for image Super Resolution". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19509/.
Texto completo da fonteCapítulos de livros sobre o assunto "L0 regularization"
Wang, Liansheng, Xinyue Li, Yiping Chen e Jing Qin. "Application of L0-Norm Regularization to Epicardial Potential Reconstruction". In Lecture Notes in Computer Science, 493–500. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24571-3_59.
Texto completo da fonteLi, Li, Fangwan Huang e Zhiyong Yu. "Echo State Network Based on L0 Norm Regularization for Chaotic Time Series Prediction". In Green, Pervasive, and Cloud Computing, 145–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64243-3_12.
Texto completo da fonteShi, Mingzhu. "A Novel Gradient L0-Norm Regularization Image Restoration Method Based on Non-local Total Variation". In Lecture Notes in Electrical Engineering, 487–93. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9409-6_57.
Texto completo da fonteTrabalhos de conferências sobre o assunto "L0 regularization"
Chen, Jun, Zemin Cai, Xiaohua Xie e Jianhuang Lai. "Motion Estimation with L0 Norm Regularization". In 2021 IEEE 7th International Conference on Virtual Reality (ICVR). IEEE, 2021. http://dx.doi.org/10.1109/icvr51878.2021.9483834.
Texto completo da fonteGuo, Yang, Tai Gao, Chengzhi Deng, Shengqian Wang e JianPing Xiao. "Sparse Unmixing using an approximate L0 Regularization". In First International Conference on Information Sciences, Machinery, Materials and Energy. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/icismme-15.2015.189.
Texto completo da fonteFormanek, Andras, e Daniel Hadhazi. "Compressing Convolutional Neural Networks by L0 Regularization". In 2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO). IEEE, 2019. http://dx.doi.org/10.1109/iccairo47923.2019.00032.
Texto completo da fonteLi, Haoxiang, e Jianmin Zheng. "L0-Regularization based Material Design for Hexahedral Mesh Models". In CAD'21. CAD Solutions LLC, 2021. http://dx.doi.org/10.14733/cadconfp.2021.314-318.
Texto completo da fonteXie, Qixin, Chao Li, Boyu Diao, Zhulin An e Yongjun Xu. "L0 Regularization based Fine-grained Neural Network Pruning Method". In 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2019. http://dx.doi.org/10.1109/ecai46879.2019.9041962.
Texto completo da fonteDe Boom, Cedric, Samuel Wauthier, Tim Verbelen e Bart Dhoedt. "Dynamic Narrowing of VAE Bottlenecks Using GECO and L0 Regularization". In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533671.
Texto completo da fonteZhenxing, Liu, e Zeng Xueying. "Mixed impulse and Gaussian noise removal using L0 sparse regularization". In Twelfth International Conference on Graphics and Image Processing, editado por Zhigeng Pan e Xinhong Hei. SPIE, 2021. http://dx.doi.org/10.1117/12.2589376.
Texto completo da fonteHan, Xiaoyu, Yannan Yang e Wende Dong. "Image denoising based on hybrid L0 and L1-norm regularization". In Novel Imaging System, editado por Bo Liu, Yan Zhou, Qiang Zhang e Feihu Xu. SPIE, 2024. http://dx.doi.org/10.1117/12.3015106.
Texto completo da fonteDelmer, Alice, Anne Ferreol e Pascal Larzabal. "On Regularization Parameter for L0-Sparse Covariance Fitting Based DOA Estimation". In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053963.
Texto completo da fonteGuo, Kaiwen, Feng Xu, Yangang Wang, Yebin Liu e Qionghai Dai. "Robust Non-rigid Motion Tracking and Surface Reconstruction Using L0 Regularization". In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.353.
Texto completo da fonte