Literatura académica sobre el tema "Joint sparsity structure"
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Artículos de revistas sobre el tema "Joint sparsity structure"
Huang, Junhao, Weize Sun y Lei Huang. "Joint Structure and Parameter Optimization of Multiobjective Sparse Neural Network". Neural Computation 33, n.º 4 (2021): 1113–43. http://dx.doi.org/10.1162/neco_a_01368.
Texto completoQin, Si, Yimin D. Zhang, Qisong Wu y Moeness G. Amin. "Structure-Aware Bayesian Compressive Sensing for Near-Field Source Localization Based on Sensor-Angle Distributions". International Journal of Antennas and Propagation 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/783467.
Texto completoLi, Meng, Liang Yan y Qianying Wang. "Group Sparse Regression-Based Learning Model for Real-Time Depth-Based Human Action Prediction". Mathematical Problems in Engineering 2018 (24 de diciembre de 2018): 1–7. http://dx.doi.org/10.1155/2018/8201509.
Texto completoBirdi, Jasleen, Audrey Repetti y Yves Wiaux. "Sparse interferometric Stokes imaging under the polarization constraint (Polarized SARA)". Monthly Notices of the Royal Astronomical Society 478, n.º 4 (4 de julio de 2018): 4442–63. http://dx.doi.org/10.1093/mnras/sty1182.
Texto completoCao, Meng, Wenxing Bao y Kewen Qu. "Hyperspectral Super-Resolution Via Joint Regularization of Low-Rank Tensor Decomposition". Remote Sensing 13, n.º 20 (14 de octubre de 2021): 4116. http://dx.doi.org/10.3390/rs13204116.
Texto completoDai, Ling-Yun, Rong Zhu y Juan Wang. "Joint Nonnegative Matrix Factorization Based on Sparse and Graph Laplacian Regularization for Clustering and Co-Differential Expression Genes Analysis". Complexity 2020 (16 de noviembre de 2020): 1–10. http://dx.doi.org/10.1155/2020/3917812.
Texto completoAbdulaziz, Abdullah, Arwa Dabbech y Yves Wiaux. "Wideband super-resolution imaging in Radio Interferometry via low rankness and joint average sparsity models (HyperSARA)". Monthly Notices of the Royal Astronomical Society 489, n.º 1 (5 de agosto de 2019): 1230–48. http://dx.doi.org/10.1093/mnras/stz2117.
Texto completoTigges, Timo, Janis Sarikas, Michael Klum y Reinhold Orglmeister. "Compressed sensing of multi-lead ECG signals by compressive multiplexing". Current Directions in Biomedical Engineering 1, n.º 1 (1 de septiembre de 2015): 65–68. http://dx.doi.org/10.1515/cdbme-2015-0017.
Texto completoGe, Ting, Tianming Zhan, Qinfeng Li y Shanxiang Mu. "Optimal Superpixel Kernel-Based Kernel Low-Rank and Sparsity Representation for Brain Tumour Segmentation". Computational Intelligence and Neuroscience 2022 (24 de junio de 2022): 1–12. http://dx.doi.org/10.1155/2022/3514988.
Texto completoGe, Ting, Ning Mu, Tianming Zhan, Zhi Chen, Wanrong Gao y Shanxiang Mu. "Brain Lesion Segmentation Based on Joint Constraints of Low-Rank Representation and Sparse Representation". Computational Intelligence and Neuroscience 2019 (1 de julio de 2019): 1–11. http://dx.doi.org/10.1155/2019/9378014.
Texto completoTesis sobre el tema "Joint sparsity structure"
Liu, Penghuan. "Statistical and numerical optimization for speckle blind structured illumination microscopy". Thesis, Ecole centrale de Nantes, 2018. http://www.theses.fr/2018ECDN0008/document.
Texto completoConventional structured illumination microscopy (SIM) can surpass the resolution limit inoptical microscopy caused by the diffraction effect, through illuminating the object with a set of perfectly known harmonic patterns. However, controlling the illumination patterns is a difficult task. Even worse, strongdistortions of the light grid can be induced by the sample within the investigated volume, which may give rise to strong artifacts in SIM reconstructed images. Recently, blind-SIM strategies were proposed, whereimages are acquired through unknown, non-harmonic,speckle illumination patterns, which are much easier to generate in practice. The super-resolution capacity of such approaches was observed, although it was not well understood theoretically. This thesis presents two new reconstruction methods in SIM using unknown speckle patterns (blind-speckle-SIM): one joint reconstruction approach and one marginal reconstruction approach. In the joint reconstruction approach, we estimate the object and the speckle patterns together by considering a basis pursuit denoising (BPDN) model with lp,q-norm regularization, with p=>1 and 0
Ramesh, Lekshmi. "Support Recovery from Linear Measurements: Tradeoffs in the Measurement-Constrained Regime". Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5500.
Texto completo"Joint Optimization of Quantization and Structured Sparsity for Compressed Deep Neural Networks". Master's thesis, 2018. http://hdl.handle.net/2286/R.I.50451.
Texto completoDissertation/Thesis
Masters Thesis Computer Engineering 2018
Actas de conferencias sobre el tema "Joint sparsity structure"
Tao, Shaozhe, Yifan Sun y Daniel Boley. "Inverse Covariance Estimation with Structured Groups". En Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/395.
Texto completoLi, Chen, Xutan Peng, Hao Peng, Jianxin Li y Lihong Wang. "TextGTL: Graph-based Transductive Learning for Semi-supervised Text Classification via Structure-Sensitive Interpolation". En Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/369.
Texto completoSun, Fangzheng, Yang Liu y Hao Sun. "Physics-informed Spline Learning for Nonlinear Dynamics Discovery". En Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/283.
Texto completoXu, Jie, Cheng Deng, Xinbo Gao, Dinggang Shen y Heng Huang. "Predicting Alzheimer's Disease Cognitive Assessment via Robust Low-Rank Structured Sparse Model". En Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/542.
Texto completoGrüttemeier, Niels y Christian Komusiewicz. "Learning Bayesian Networks Under Sparsity Constraints: A Parameterized Complexity Analysis". En Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/586.
Texto completoOliveira, Saullo H. G., André R. Gonçalves y Fernando J. Von Zuben. "Group LASSO with Asymmetric Structure Estimation for Multi-Task Learning". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/444.
Texto completoWang, Zhangyang, Shuai Huang, Jiayu Zhou y Thomas S. Huang. "Doubly Sparsifying Network". En Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/421.
Texto completoLiu, Yanchi, Tan Yan y Haifeng Chen. "Exploiting Graph Regularized Multi-dimensional Hawkes Processes for Modeling Events with Spatio-temporal Characteristics". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/343.
Texto completoWang, Zihan, Zhaochun Ren, Chunyu He, Peng Zhang y Yue Hu. "Robust Embedding with Multi-Level Structures for Link Prediction". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/728.
Texto completoZhong, Wanjun, Junjie Huang, Qian Liu, Ming Zhou, Jiahai Wang, Jian Yin y Nan Duan. "Reasoning over Hybrid Chain for Table-and-Text Open Domain Question Answering". En Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/629.
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