Auswahl der wissenschaftlichen Literatur zum Thema „Convolutional Dictionary Learning (CDL)“
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Zeitschriftenartikel zum Thema "Convolutional Dictionary Learning (CDL)"
Li, Jing, Xiao Wei, Fengpin Wang und Jinjia Wang. „IPGM: Inertial Proximal Gradient Method for Convolutional Dictionary Learning“. Electronics 10, Nr. 23 (03.12.2021): 3021. http://dx.doi.org/10.3390/electronics10233021.
Der volle Inhalt der QuelleTurquais, Pierre, Endrias G. Asgedom und Walter Söllner. „A method of combining coherence-constrained sparse coding and dictionary learning for denoising“. GEOPHYSICS 82, Nr. 3 (01.05.2017): V137—V148. http://dx.doi.org/10.1190/geo2016-0164.1.
Der volle Inhalt der QuelleSong, Andrew, Bahareh Tolooshams und Demba Ba. „Gaussian Process Convolutional Dictionary Learning“. IEEE Signal Processing Letters 29 (2022): 95–99. http://dx.doi.org/10.1109/lsp.2021.3127471.
Der volle Inhalt der QuelleGao, Fangyuan, Xin Deng, Mai Xu, Jingyi Xu und Pier Luigi Dragotti. „Multi-Modal Convolutional Dictionary Learning“. IEEE Transactions on Image Processing 31 (2022): 1325–39. http://dx.doi.org/10.1109/tip.2022.3141251.
Der volle Inhalt der QuelleChun, Il Yong, und Jeffrey A. Fessler. „Convolutional Dictionary Learning: Acceleration and Convergence“. IEEE Transactions on Image Processing 27, Nr. 4 (April 2018): 1697–712. http://dx.doi.org/10.1109/tip.2017.2761545.
Der volle Inhalt der QuelleSong, Andrew H., Francisco J. Flores und Demba Ba. „Convolutional Dictionary Learning With Grid Refinement“. IEEE Transactions on Signal Processing 68 (2020): 2558–73. http://dx.doi.org/10.1109/tsp.2020.2986897.
Der volle Inhalt der QuelleWan, Fucheng, Dengyun Zhu, Xiangzhen He, Qi Guo, Dongjiao Zhang, Zhenyang Ren und Yuxiang Du. „Agricultural Product Recommendation Model based on BMF“. Applied Mathematics and Nonlinear Sciences 5, Nr. 2 (01.07.2020): 415–24. http://dx.doi.org/10.2478/amns.2020.2.00060.
Der volle Inhalt der QuelleMansha, Sameen, Hoang Thanh Lam, Hongzhi Yin, Faisal Kamiran und Mohsen Ali. „Layered convolutional dictionary learning for sparse coding itemsets“. World Wide Web 22, Nr. 5 (11.05.2018): 2225–39. http://dx.doi.org/10.1007/s11280-018-0565-2.
Der volle Inhalt der QuelleMolla, Md Rony, und Ma Jian Fen. „Convolutional Sparse Coding Multiple Instance Learning for Whole Slide Image Classification“. International Journal of Advanced Engineering Research and Science 10, Nr. 12 (2023): 096–104. http://dx.doi.org/10.22161/ijaers.1012.10.
Der volle Inhalt der QuelleHumbert, Pierre, Laurent Oudre, Nicolas Vayatis und Julien Audiffren. „Tensor Convolutional Dictionary Learning With CP Low-Rank Activations“. IEEE Transactions on Signal Processing 70 (2022): 785–96. http://dx.doi.org/10.1109/tsp.2021.3135695.
Der volle Inhalt der QuelleDissertationen zum Thema "Convolutional Dictionary Learning (CDL)"
Allain, Cédric. „Temporal point processes and scalable convolutional dictionary learning : a unified framework for m/eeg signal analysis in neuroscience“. Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG008.
Der volle Inhalt der QuelleIn the field of non-invasive brain imaging, Magnetoencephalography and Electroencephalography (M/EEG) offer invaluable insights into neural activities. The recorded data consist of multivariate time series that provide information about cognitive processes and are often complemented by auxiliary details related to the experimental paradigm, such as timestamps of external stimuli or actions undertaken by the subjects. Additionally, the dataset may include recordings from multiple subjects, facilitating population- level analyses.This doctoral research presents a novel framework for M/EEG signal analysis that synergizes Convolutional Dictionary Learning (CDL) and Temporal Point Processes (TPPs). The work is segmented into two primary components: temporal modeling advancements and computational scalability. For temporal modeling, two novel point process models are introduced with efficient inference methods to capture task-specific neural activities. The proposed Fast Discretized Inference for Hawkes Processes (FaDIn) method also has implications for broader applications. Additionally, this work addresses the computational challenges of large-scale M/EEG data CDL-based analysis, by introducing a novel Stochastic Robust Windowing CDL algorithm. This algorithm allows to process efficiently artifact-ridden signals as well as large population studies. Population CDL was then used on the large open-access dataset Cam-CAN, shedding light on age-related neural activity
Quesada, Pacora Jorge Gerardo. „Separable dictionary learning for convolutional sparse coding via split updates“. Master's thesis, Pontificia Universidad Católica del Perú, 2019. http://hdl.handle.net/20.500.12404/14209.
Der volle Inhalt der QuelleTesis
Silva, Obregón Gustavo Manuel. „Efficient algorithms for convolutional dictionary learning via accelerated proximal gradient“. Master's thesis, Pontificia Universidad Católica del Perú, 2019. http://hdl.handle.net/20.500.12404/13903.
Der volle Inhalt der QuelleTesis
Moreau, Thomas. „Représentations Convolutives Parcimonieuses -- application aux signaux physiologiques et interpétabilité de l'apprentissage profond“. Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLN054/document.
Der volle Inhalt der QuelleConvolutional representations extract recurrent patterns which lead to the discovery of local structures in a set of signals. They are well suited to analyze physiological signals which requires interpretable representations in order to understand the relevant information. Moreover, these representations can be linked to deep learning models, as a way to bring interpretability intheir internal representations. In this disserta tion, we describe recent advances on both computational and theoretical aspects of these models.First, we show that the Singular Spectrum Analysis can be used to compute convolutional representations. This representation is dense and we describe an automatized procedure to improve its interpretability. Also, we propose an asynchronous algorithm, called DICOD, based on greedy coordinate descent, to solve convolutional sparse coding for long signals. Our algorithm has super-linear acceleration.In a second part, we focus on the link between representations and neural networks. An extra training step for deep learning, called post-training, is introduced to boost the performances of the trained network by making sure the last layer is optimal. Then, we study the mechanisms which allow to accelerate sparse coding algorithms with neural networks. We show that it is linked to afactorization of the Gram matrix of the dictionary.Finally, we illustrate the relevance of convolutional representations for physiological signals. Convolutional dictionary learning is used to summarize human walk signals and Singular Spectrum Analysis is used to remove the gaze movement in young infant’s oculometric recordings
Buchteile zum Thema "Convolutional Dictionary Learning (CDL)"
Sandilya, Mrinmoy, und S. R. Nirmala. „Compressed Sensing MRI Reconstruction Using Convolutional Dictionary Learning and Laplacian Prior“. In IOT with Smart Systems, 661–69. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-3945-6_65.
Der volle Inhalt der QuelleZhang, Chengfang, und Xingchun Yang. „Image Fusion Based on Masked Online Convolutional Dictionary Learning with Surrogate Function Approach“. In Advances in Intelligent Systems and Computing, 70–74. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5887-0_10.
Der volle Inhalt der QuelleElbasiony, Reda, Walid Gomaa und Tetsuya Ogata. „Deep 3D Pose Dictionary: 3D Human Pose Estimation from Single RGB Image Using Deep Convolutional Neural Network“. In Artificial Neural Networks and Machine Learning – ICANN 2018, 310–20. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01424-7_31.
Der volle Inhalt der QuelleZhang, Chengfang, und Xingchun Yang. „Visible and Infrared Image Fusion Based on Online Convolutional Dictionary Learning with Sparse Matrix Computation“. In Advances in Wireless Communications and Applications, 123–28. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5697-5_15.
Der volle Inhalt der QuelleZhang, Chengfang, und Xingchun Yang. „Visible and Infrared Image Fusion Based on Masked Online Convolutional Dictionary Learning with Frequency Domain Computation“. In New Developments of IT, IoT and ICT Applied to Agriculture, 177–82. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5073-7_18.
Der volle Inhalt der QuelleDong, Qunxi, Jie Zhang, Qingyang Li, Pau M. Thompson, Richard J. Caselli, Jieping Ye und Yalin Wang. „Multi-task Dictionary Learning Based on Convolutional Neural Networks for Longitudinal Clinical Score Predictions in Alzheimer’s Disease“. In Human Brain and Artificial Intelligence, 21–35. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1398-5_2.
Der volle Inhalt der Quelle„Convolutional Dictionary Learning“. In Computer Vision, 214. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63416-2_300327.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Convolutional Dictionary Learning (CDL)"
Chun, Il Yong, und Jeffrey A. Fessler. „Convergent convolutional dictionary learning using Adaptive Contrast Enhancement (CDL-ACE): Application of CDL to image denoising“. In 2017 International Conference on Sampling Theory and Applications (SampTA). IEEE, 2017. http://dx.doi.org/10.1109/sampta.2017.8024378.
Der volle Inhalt der QuelleZhang, Chengfang. „Convolutional Dictionary Learning Using Global Matching Tracking (CDL-GMT): Application to Visible-Infrared Image Fusion“. In 2020 4th Annual International Conference on Data Science and Business Analytics (ICDSBA). IEEE, 2020. http://dx.doi.org/10.1109/icdsba51020.2020.00081.
Der volle Inhalt der QuelleLiu, Jialin, Cristina Garcia-Cardona, Brendt Wohlberg und Wotao Yin. „Online convolutional dictionary learning“. In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296573.
Der volle Inhalt der QuellePapyan, Vardan, Yaniv Romano, Michael Elad und Jeremias Sulam. „Convolutional Dictionary Learning via Local Processing“. In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017. http://dx.doi.org/10.1109/iccv.2017.566.
Der volle Inhalt der QuelleZazo, Javier, Bahareh Tolooshams, Demba Ba und Harvard John A. Paulson. „Convolutional Dictionary Learning in Hierarchical Networks“. In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2019. http://dx.doi.org/10.1109/camsap45676.2019.9022440.
Der volle Inhalt der QuelleGarcia-Cardona, Cristina, und Brendt Wohlberg. „Subproblem coupling in convolutional dictionary learning“. In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296571.
Der volle Inhalt der QuelleXu, Lijun, Ying Wang und Yijia Zhou. „Dictionary learning in convolutional sparse representation“. In Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), herausgegeben von Shi Jin und Wanyang Dai. SPIE, 2023. http://dx.doi.org/10.1117/12.2672444.
Der volle Inhalt der QuelleGarcia-Cardona, Cristina, und Brendt Wohlberg. „Convolutional Dictionary Learning for Multi-Channel Signals“. In 2018 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2018. http://dx.doi.org/10.1109/acssc.2018.8645108.
Der volle Inhalt der QuelleSkau, Erik, und Cristina Garcia-Cardona. „Tomographic Reconstruction Via 3D Convolutional Dictionary Learning“. In 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP). IEEE, 2018. http://dx.doi.org/10.1109/ivmspw.2018.8448657.
Der volle Inhalt der QuelleDegraux, Kevin, Ulugbek S. Kamilov, Petros T. Boufounos und Dehong Liu. „Online convolutional dictionary learning for multimodal imaging“. In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296555.
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