Results 271 to 280 of about 154,546 (314)

ASCA-YOLO: Adaptive Sparse and Context-Aware YOLO Algorithm for Forest Wildfire Detection. [PDF]

Sensors (Basel)
Hao Y, Wang K, Zhang L, Yuan Z.
europepmc   +1 more source

Predicting the Effort Required to Manually Mend Auto-Segmentations. [PDF]

IEEE J Biomed Health Inform
He D, Tong Y, Torigian DA, Udupa JK.
europepmc   +1 more source

A dynamic element-activated non-semantic sparse attention method for remote sensing small object detection. [PDF]

Sci Rep
Liu S, Bie Y, Dong Y, He Y, Liu Z, Wang J.   +5 more
europepmc   +1 more source

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Adaptively Sparse Transformers Hawkes Process

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2023
Nowadays, many sequences of events are generated in areas as diverse as healthcare, finance, and social network. People have been studying these data for a long time. They hope to predict the type and occurrence time of the next event by using relationships among events in the data. recently, with the successful application of Recurrent Neural Network
Yue Gao, Jian-Wei Liu 0006
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Applications of sparse signal processing

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016
Sparse signal processing has found various applications in different research areas where the sparsity of the signal of interest plays a significant role in addressing their ill-posedness. In this invited paper, we give a brief review of a number of such applications in inverse scattering of microwave medical imaging, compressed video sensing, and ...
Masoumeh Azghani, Farokh Marvasti
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Group Sparse Optimal Transport for Sparse Process Flexibility Design

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023
As a fundamental problem in Operations Research, sparse process flexibility design (SPFD) aims to design a manufacturing network across industries that achieves a trade-off between the efficiency and robustness of supply chains. In this study, we propose a novel solution to this problem with the help of computational optimal transport techniques ...
Dixin Luo, Tingting Yu, Hongteng Xu
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SPICE: A Sparse Covariance-Based Estimation Method for Array Processing

IEEE Transactions on Signal Processing, 2011
This paper presents a novel SParse Iterative Covariance-based Estimation approach, abbreviated as SPICE, to array processing. The proposed approach is obtained by the minimization of a covariance matrix fitting criterion and is particularly useful in ...
Petre Stoica, Prabhu Babu, Jian Li
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Sparse Multimodal Gaussian Processes

2017
Gaussian processes (GPs) are effective tools in machine learning. Unfortunately, due to their unfavorable scaling, a more widespread use has probably been impeded. By leveraging sparse approximation methods, sparse Gaussian processes extend the applicability of GPs to a richer data. Multimodal data are common in machine learning applications.
Qiuyang Liu, Shiliang Sun
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