Results 271 to 280 of about 603,409 (311)

Multi-Task Learning for Joint Indoor Localization and Blind Channel Estimation in OFDM Systems. [PDF]

Sensors (Basel)
Molina MC, Ahriz I, Zerioul L, Terré M.
europepmc   +1 more source

A Paralleled Multi-Task Learning-Based Framework for Single-Lead ECG Fine-Grained Noise Localization, Denoising and Signal Quality Assessment. [PDF]

Sensors (Basel)
Hu Y, Liu Q, Zhou Z, Xu W, Tang H.
europepmc   +1 more source

A lightweight transformer based multi task learning model with dynamic weight allocation for improved vulnerability prediction. [PDF]

Sci Rep
Liu L, Hui Z, Chen G, Cai T, Zhou C.
europepmc   +1 more source

Novel multi-task learning for Alzheimer's stage classification using hippocampal MRI segmentation, feature fusion, and nomogram modeling. [PDF]

Eur J Med Res
Hu W, Du Q, Wei L, Wang D, Zhang G.
europepmc   +1 more source

EmoShiftNet: a shift-aware multi-task learning framework with fusion strategies for emotion recognition in multi-party conversations. [PDF]

Front Artif Intell
Nirujan H, Priyadarshana YHPP.
europepmc   +1 more source

Auto-branch multi-task learning for simultaneous prediction of multiple correlated traits associated with Alzheimer's disease. [PDF]

Front Genet
Liang J, Xue Z, Zhou W, Guo X, Wen Y.
europepmc   +1 more source

An overview of multi-task learning

National Science Review, 2018
As a promising area in machine learning, multi-task learning (MTL) aims to improve the performance of multiple related learning tasks by leveraging useful information among them.
Yang Qiang
exaly   +3 more sources

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Calibrated Multi-Task Learning

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018
This paper proposes a novel algorithm, named Non-Convex Calibrated Multi-Task Learning (NC-CMTL), for learning multiple related regression tasks jointly. Instead of utilizing the nuclear norm, NC-CMTL adopts a non-convex low rank regularizer to explore the shared information among different tasks.
Feiping Nie 0001, Zhanxuan Hu, Xuelong Li 0001   +2 more
openaire   +1 more source

Regularized multi--task learning

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 2004
Past empirical work has shown that learning multiple related tasks from data simultaneously can be advantageous in terms of predictive performance relative to learning these tasks independently. In this paper we present an approach to multi--task learning based on the minimization of regularization functionals similar to existing ones, such as the one ...
Theodoros Evgeniou, Massimiliano Pontil
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Parallel Multi-task Learning

2015 IEEE International Conference on Data Mining, 2015
In this paper, we develop parallel algorithms for a family of regularized multi-task methods which can model task relations under the regularization framework. Since those multi-task methods cannot be parallelized directly, we use the FISTA algorithm, which in each iteration constructs a surrogate function of the original problem by utilizing the ...
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