Results 281 to 290 of about 603,409 (311)

Multi-task Learning with Riemannian Optimization

2021
Multi-task learning (MTL) is a promising research field of machine learning, in which the training process of the neural network is equivalent to multi-objective optimization. On one hand, MTL trains all the network weights simultaneously to converge the multi-task loss. On the other hand, multi-objective optimization aims to find the optimum solution,
Tian Cai, Liang Song, Guilin Li, Minghong Liao   +3 more
openaire   +1 more source

Learning Tree Structure in Multi-Task Learning

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015
In multi-task learning (MTL), multiple related tasks are learned jointly by sharing information according to task relations. One promising approach is to utilize the given tree structure, which describes the hierarchical relations among tasks, to learn model parameters under the regularization framework.
Lei Han 0001, Yu Zhang 0006
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A brief review on multi-task learning

Multimedia Tools and Applications, 2018
Multi-task learning (MTL), which optimizes multiple related learning tasks at the same time, has been widely used in various applications, including natural language processing, speech recognition, computer vision, multimedia data processing, biomedical imaging, socio-biological data analysis, multi-modality data analysis, etc.
Kim-Han Thung, Chong-Yaw Wee
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Learning a Kernel for Multi-Task Clustering

Proceedings of the AAAI Conference on Artificial Intelligence, 2011
Multi-task learning has received increasing attention in the past decade. Many supervised multi-task learning methods have been proposed, while unsupervised multi-task learning is still a rarely studied problem. In this paper, we propose to learn a kernel for multi-task clustering. Our goal is to learn a Reproducing Kernel Hilbert Space,
Quanquan Gu, Zhenhui Li, Jiawei Han 0001
openaire   +1 more source

Collaborative filtering by multi-task learning

2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies, 2008
Collaborative filtering is a technique to predict userspsila interests for items by exploiting the behavior patterns of a group of users with similar preferences. This technique has been widely used for recommender systems and has a number of successful applications in E-commerce.
Nguyen Duy Phuong, Tu Minh Phuong
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Multi-task Learning

2018
Traditional supervised machine learning methods involve learning mapping function that can accurately map input data to output label. However, real-world datasets are complex, and we often encounter situations where multiple tasks (classes) are related to each other.
Azad Naik, Huzefa Rangwala
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Multi-task Learning by Pareto Optimality

2019
Deep Neural Networks (DNNs) are often criticized because they lack the ability to learn more than one task at a time: Multitask Learning is an emerging research area whose aim is to overcome this issue. In this work, we introduce the Pareto Multitask Learning framework as a tool that can show how effectively a DNN is learning a shared representation ...
Dyankov D., Riccio S. D., Di Fatta G., Nicosia G.   +3 more
openaire   +2 more sources

Manifold Regularized Multi-Task Learning

2012
Multi-task learning (MTL) has drawn a lot of attentions in machine learning. By training multiple tasks simultaneously, information can be better shared across tasks. This leads to significant performance improvement in many problems. However, most existing methods assume that all tasks are related or their relationship follows a simple and specified ...
Peipei Yang, Xu-Yao Zhang, Kaizhu Huang, Cheng-Lin Liu 0001   +3 more
openaire   +1 more source

Binaural audio generation via multi-task learning

ACM Transactions on Graphics, 2021
Shiguang Liu, Dinesh Manocha, Manochadinesh   +2 more
exaly  

Residual multi-task learning for facial landmark localization and expression recognition

Pattern Recognition, 2021
Boyu Chen, Huchuan Lu
exaly