Results 31 to 40 of about 1,489,144 (280)
Boosted multi-task learning [PDF]
Machine Learning, 2010 In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing their commonalities through shared parameters and their differences with task-specific ones. This enables implicit data sharing and regularization.
Olivier Chapelle +5 more
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An efficient deep multi‐task learning structure for covid‐19 disease
IET Image Processing, 2023 COVID‐19 has had a profound global impact, necessitating the development of infection detection systems based on machine learning. This paper presents a Multi‐task architecture that addresses the classification and segmentation tasks for COVID‐19 ...
Shirin Kordnoori +3 more
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Understanding and Application of Multi-Task Learning in Medical Artificial Intelligence
대한영상의학회지, 2022 In the medical field, artificial intelligence has been used in various ways with many developments. However, most artificial intelligence technologies are developed so that one model can perform only one task, which is a limitation in designing the ...
Young Jae Kim, Kwang Gi Kim
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Multiple object tracking based on multi‐task learning with strip attention
IET Image Processing, 2021 Multiple object tracking (MOT) framework based on bifurcate strategy was usually challenged by data association of different model path, which work for object localisation and appearance embedding independently. By incorporating the re‐identification (re‐
Yaoye Song +5 more
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Resource-Efficient Multi-Task Deep Learning Using a Multi-Path Network
IEEE Access, 2022 Multi-task learning (MTL) improves learning efficiency compared to the single-task counterpart in that it performs multiple tasks at the same time. Due to the nature, it can achieve generalized performance as well as alleviate overfitting.
Soyeon Park, Jiho Lee, Eunwoo Kim
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Learning Output Kernels for Multi-Task Problems [PDF]
, 2013 Simultaneously solving multiple related learning tasks is beneficial under a variety of circumstances, but the prior knowledge necessary to correctly model task relationships is rarely available in practice. In this paper, we develop a novel kernel-based
Dinuzzo, Francesco
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, 2023 Multi-task learning (MTL) has achieved great success in various research domains, such as CV, NLP and IR etc. Due to the complex and competing task correlation, naive training all tasks may lead to inequitable learning, i.e. some tasks are learned well while others are overlooked.
Yuan, Jun, Zhang, Rui
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Adaptive and robust multi-task learning
The Annals of Statistics, 2023 We study the multi-task learning problem that aims to simultaneously analyze multiple datasets collected from different sources and learn one model for each of them. We propose a family of adaptive methods that automatically utilize possible similarities among those tasks while carefully handling their differences.
Duan, Yaqi, Wang, Kaizheng
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Multi-Task Multi-Sample Learning [PDF]
, 2015 In the exemplar SVM (E-SVM) approach of Malisiewicz et al., ICCV 2011, an ensemble of SVMs is learnt, with each SVM trained independently using only a single positive sample and all negative samples for the class. In this paper we develop a multi-sample learning (MSL) model which enables joint regularization of the E-SVMs without any additional cost ...
Aytar, Y, Zisserman, A
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Geometry preserving multi-task metric learning [PDF]
Machine Learning, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yang, Peipei +2 more
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