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Variational data assimilation via sparse regularisation [PDF]
Tellus: Series A, Dynamic Meteorology and Oceanography, 2014 This paper studies the role of sparse regularisation in a properly chosen basis for variational data assimilation (VDA) problems. Specifically, it focuses on data assimilation of noisy and down-sampled observations while the state variable of interest ...
Ardeshir M. Ebtehaj +3 more
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Blind Image Deblurring via a Novel Sparse Channel Prior
Mathematics, 2022 Blind image deblurring (BID) is a long-standing challenging problem in low-level image processing. To achieve visually pleasing results, it is of utmost importance to select good image priors. In this work, we develop the ratio of the dark channel prior (
Dayi Yang, Xiaojun Wu, Hefeng Yin
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Social-sparsity brain decoders: faster spatial sparsity [PDF]
2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2016 Spatially-sparse predictors are good models for brain decoding: they give accurate predictions and their weight maps are interpretable as they focus on a small number of regions. However, the state of the art, based on total variation or graph-net, is computationally costly. Here we introduce sparsity in the local neighborhood of each voxel with social-
Varoquaux, Gaël +2 more
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A Sparse EEG-Informed fMRI Model for Hybrid EEG-fMRI Neurofeedback Prediction
Frontiers in Neuroscience, 2020 Measures of brain activity through functional magnetic resonance imaging (fMRI) or electroencephalography (EEG), two complementary modalities, are ground solutions in the context of neurofeedback (NF) mechanisms for brain rehabilitation protocols.
Claire Cury +4 more
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Tensor Rank Regularization with Bias Compensation for Millimeter Wave Channel Estimation
Signals, 2022 This paper presents a novel method of tensor rank regularization with bias compensation for channel estimation in a hybrid millimeter wave MIMO-OFDM system.
Fei He, Andrew Harms, Lamar Yaoqing Yang
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Structured Sparsity of Convolutional Neural Networks via Nonconvex Sparse Group Regularization
Frontiers in Applied Mathematics and Statistics, 2021 Convolutional neural networks (CNN) have been hugely successful recently with superior accuracy and performance in various imaging applications, such as classification, object detection, and segmentation.
Kevin Bui +4 more
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Nonparametric sparsity and regularization
J. Mach. Learn. Res., 2012 45 pages, 11 ...
ROSASCO, LORENZO +4 more
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Inference for relative sparsity
CoRR, 2023 66 pages, 3 ...
Samuel J. Weisenthal +2 more
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Manifold Discovery for High-Dimensional Data Using Deep Method
IEEE Access, 2022 It is a challenge for manifold discovery from the data in the high-dimensional space, since the data in the high-dimensional space is sparsely distributed, which hardly provides rich information for manifold discovery so as to be possible to obtain ...
Jingjin Chen, Shuping Chen, Xuan Ding
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Using Regularization to Infer Cell Line Specificity in Logical Network Models of Signaling Pathways
Frontiers in Physiology, 2018 Understanding the functional properties of cells of different origins is a long-standing challenge of personalized medicine. Especially in cancer, the high heterogeneity observed in patients slows down the development of effective cures.
Sébastien De Landtsheer +2 more
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