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Добірка наукової літератури з теми "Deep CCA"

Автор: Grafiati

Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Deep CCA"

1

Lindenbaum, Ofir, Neta Rabin, Yuri Bregman, and Amir Averbuch. "Seismic Event Discrimination Using Deep CCA." IEEE Geoscience and Remote Sensing Letters 17, no. 11 (2020): 1856–60. http://dx.doi.org/10.1109/lgrs.2019.2959554.

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2

Karami, Mahdi, and Dale Schuurmans. "Deep Probabilistic Canonical Correlation Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 8055–63. http://dx.doi.org/10.1609/aaai.v35i9.16982.

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Анотація:

We propose a deep generative framework for multi-view learning based on a probabilistic interpretation of canonical correlation analysis (CCA). The model combines a linear multi-view layer in the latent space with deep generative networks as observation models, to decompose the variability in multiple views into a shared latent representation that describes the common underlying sources of variation and a set of viewspecific components. To approximate the posterior distribution of the latent multi-view layer, an efficient variational inference procedure is developed based on the solution of pr

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3

Gao, Quanxue, Huanhuan Lian, Qianqian Wang, and Gan Sun. "Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3938–45. http://dx.doi.org/10.1609/aaai.v34i04.5808.

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For cross-modal subspace clustering, the key point is how to exploit the correlation information between cross-modal data. However, most hierarchical and structural correlation information among cross-modal data cannot be well exploited due to its high-dimensional non-linear property. To tackle this problem, in this paper, we propose an unsupervised framework named Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis (CMSC-DCCA), which incorporates the correlation constraint with a self-expressive layer to make full use of information among the inter-modal data and the intra

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4

Li, Bin, and Yuqing He. "Computational Logistics for Container Terminal Handling Systems with Deep Learning." Computational Intelligence and Neuroscience 2021 (April 26, 2021): 1–18. http://dx.doi.org/10.1155/2021/5529914.

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Container terminals are playing an increasingly important role in the global logistics network; however, the programming, planning, scheduling, and decision of the container terminal handling system (CTHS) all are provided with a high degree of nonlinearity, coupling, and complexity. Given that, a combination of computational logistics and deep learning, which is just about container terminal-oriented neural-physical fusion computation (CTO-NPFC), is proposed to discuss and explore the pattern recognition and regression analysis of CTHS. Because the liner berthing time (LBT) is the central ind

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5

Jain, Pankaj K., Abhishek Dubey, Luca Saba, et al. "Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm." Journal of Cardiovascular Development and Disease 9, no. 10 (2022): 326. http://dx.doi.org/10.3390/jcdd9100326.

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Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experimen

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6

Jiang, Guoping, Wu Zhang, Ting Wang, et al. "Characteristics of genomic alterations in Chinese cholangiocarcinoma patients." Japanese Journal of Clinical Oncology 50, no. 10 (2020): 1117–25. http://dx.doi.org/10.1093/jjco/hyaa088.

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Abstract Objective Cholangiocarcinoma (CCA) is a primary malignancy, which is often diagnosed as advanced and inoperable due to the lack of effective biomarkers and poor sensitivity of clinical diagnosis. Here, we aimed to identify the genomic profile of CCA and provided molecular evidence for further biomarker development. Methods The formalin-fixed paraffin-embedded and matching blood samples were sequenced by deep sequencing targeting 450 cancer genes and genomic alteration analysis was performed. Tumor mutational burden (TMB) was measured by an algorithm developed in-house. Correlation ana

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7

Yuan, Fei, Xiaoquan Ke, and En Cheng. "Joint Representation and Recognition for Ship-Radiated Noise Based on Multimodal Deep Learning." Journal of Marine Science and Engineering 7, no. 11 (2019): 380. http://dx.doi.org/10.3390/jmse7110380.

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Ship recognition based on ship-radiated noise is one of the most important and challenging subjects in underwater acoustic signal processing. The recognition methods for ship-radiated noise recognition include traditional methods and deep learning (DL) methods. Developing from the DL methods and inspired by audio–video speech recognition (AVSR), the paper further introduces multimodal deep learning (multimodal-DL) methods for the recognition of ship-radiated noise. In this paper, ship-radiated noise (acoustics modality) and visual observation of the ships (visual modality) are two different mo

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8

Chapman, James, and Hao-Ting Wang. "CCA-Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic CCA methods in a scikit-learn style framework." Journal of Open Source Software 6, no. 68 (2021): 3823. http://dx.doi.org/10.21105/joss.03823.

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9

Yu, Yi, Suhua Tang, Kiyoharu Aizawa, and Akiko Aizawa. "Category-Based Deep CCA for Fine-Grained Venue Discovery From Multimodal Data." IEEE Transactions on Neural Networks and Learning Systems 30, no. 4 (2019): 1250–58. http://dx.doi.org/10.1109/tnnls.2018.2856253.

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10

Peng, Yun, Shenyi Zhao, and Jizhan Liu. "Fused Deep Features-Based Grape Varieties Identification Using Support Vector Machine." Agriculture 11, no. 9 (2021): 869. http://dx.doi.org/10.3390/agriculture11090869.

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Proper identification of different grape varieties by smart machinery is of great importance to modern agriculture production. In this paper, a fast and accurate identification method based on Canonical Correlation Analysis (CCA), which can fuse different deep features extracted from Convolutional Neural Network (CNN), plus Support Vector Machine (SVM) is proposed. In this research, based on an open dataset, three types of state-of-the-art CNNs, seven species of deep features, and a multi-class SVM classifier were studied. First, the images were resized to meet the input requirements of a CNN.

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