Results 61 to 70 of about 7,496 (160)

Integrative analysis of multiple diverse omics datasets by sparse group multitask regression

Frontiers in Cell and Developmental Biology, 2014
A variety of high throughput genome-wide assays enable the exploration of genetic risk factors underlying complex traits. Although these studies have remarkable impact on identifying susceptible biomarkers, they suffer from issues such as limited sample ...
Dongdong eLin, Dongdong eLin, Jigang eZhang, Jigang eZhang, Jingyao eLi, Jingyao eLi, Hao eHe, Hao eHe, hong-wen eDeng, hong-wen eDeng, Yu-Ping eWang, Yu-Ping eWang, Yu-Ping eWang   +12 more
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Multitask and Transfer Learning Approach for Joint Classification and Severity Estimation of Dysphonia

IEEE Journal of Translational Engineering in Health and Medicine
Objective: Despite speech being the primary communication medium, it carries valuable information about a speaker’s health, emotions, and identity. Various conditions can affect the vocal organs, leading to speech difficulties.
Dosti Aziz, Sztaho David
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Multitask Level-Based Learning Swarm Optimizer

Biomimetics
Evolutionary multitasking optimization (EMTO) is currently one of the hottest research topics that aims to utilize the correlation between tasks to optimize them simultaneously. Although many evolutionary multitask algorithms (EMTAs) based on traditional differential evolution (DE) and the genetic algorithm (GA) have been proposed, there are relatively
Jiangtao Chen, Zijia Wang, Zheng Kou
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The Benefit of Multitask Representation Learning

, 2015
We discuss a general method to learn data representations from multiple tasks. We provide a justification for this method in both settings of multitask learning and learning-to-learn. The method is illustrated in detail in the special case of linear feature learning.
Maurer, A, Pontil, M, Romera-Paredes, B
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MM-HiFuse: multi-modal multi-task hierarchical feature fusion for esophagus cancer staging and differentiation classification

Complex & Intelligent Systems
Esophageal cancer is a globally significant but understudied type of cancer with high mortality rates. The staging and differentiation of esophageal cancer are crucial factors in determining the prognosis and surgical treatment plan for patients, as well
Xiangzuo Huo, Shengwei Tian, Long Yu, Wendong Zhang, Aolun Li, Qimeng Yang, Jinmiao Song   +6 more
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Learning to Transfer for Evolutionary Multitasking

IEEE Transactions on Cybernetics
Under ...
Sheng-Hao Wu, Yuxiao Huang, Xingyu Wu, Liang Feng, Zhi-Hui Zhan, Kay Chen Tan   +5 more
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Anterograde interference in multitask perceptual learning

npj Science of Learning
Learning to perform multiple tasks robustly is a crucial facet of human intelligence, yet its mechanisms remain elusive. Here, we formulated four hypotheses concerning task interactions and investigated them by analyzing training sequence effects through a continual learning framework.
Jia Yang, Fang-Fang Yan, Tingting Wang, Zile Wang, Qingshang Ma, Jinmei Xiao, Xianyuan Yang, Zhong-Lin Lu, Chang-Bing Huang   +8 more
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Multi-task learning for estimation of remote PPG and respiration signals with complex valued convolutional neural network

Scientific Reports
Remote and continuous biometric signal monitoring has become increasingly crucial for the prompt diagnosis of physiological disorders. However, traditional contact sensors might pose the risk of virus spread and cause discomfort, thereby impeding the ...
Junghwan Lee, YuSang Nam, Jihwan Won, Suhwan Baek, Donggyu Sim, Ryanghee Sohn, Cheolsoo Park   +6 more
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Multitask Learning with Learned Task Relationships

Classical consensus-based strategies for federated and decentralized learning are statistically suboptimal in the presence of heterogeneous local data or task distributions. As a result, in recent years, there has been growing interest in multitask or personalized strategies, which allow individual agents to benefit from one another in pursuing locally
Wan, Zirui, Vlaski, Stefan
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An automatic pruning method for SAR target detection based on multitask reinforcement learning

International Journal of Applied Earth Observations and Geoinformation
In recent years, research on synthetic aperture radar (SAR) target detection based on deep learning methods has made substantial progress in model accuracy.
Huiyao Wan, Jie Chen, Pazlat Nurmamat, Hongcheng Zeng, Wei Yang, Tao Xu, Bocai Wu, Jie Chen, Zhixiang Huang, Paulo S.R. Diniz   +9 more
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