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A Survey on Multi-Task Learning [PDF]

IEEE Transactions on Knowledge and Data Engineering, 2022
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL from the perspective of algorithmic modeling, applications and theoretical analyses.
Yu Zhang, Yang Qiang
exaly   +4 more sources

Sparse multi-task reinforcement learning [PDF]

Intelligenza Artificiale, 2015
Abstract In multi-task reinforcement learning (MTRL), the objective is to simultaneously learn multiple tasks and exploit their similarity to improve the performance w.r.t. single-task learning. In this paper we investigate the case when all the tasks can be accurately represented in a linear approximation space using the same small subset of the ...
Calandriello, Daniele, Lazaric, Alessandro, RESTELLI, MARCELLO   +2 more
openaire   +7 more sources

Multi task learning based early prediction model for antibiotic resistance using multi institutional cohort data [PDF]

Scientific Reports
Antibiotic resistance poses a significant global health challenge, with its rapid emergence driven by inappropriate antibiotic use. This study aimed to develop and compare machine learning models to predict resistance to nine antibiotic classes in ...
Yeongmin Kim, Inyong Jeong, Jin-Hyun Park, Taewon Jung, Jang-Wook Sohn, Hongseok Park, June Choi, Se Yoon Park, Hwamin Lee   +8 more
doaj   +2 more sources

A Comparison of Loss Weighting Strategies for Multi task Learning in Deep Neural Networks

IEEE Access, 2019
With the success of deep learning in a wide variety of areas, many deep multi-task learning (MTL) models have been proposed claiming improvements in performance obtained by sharing the learned structure across several related tasks. However, the dynamics
Ting Gong, Tyler Lee, Cory Stephenson, Venkata Renduchintala, Suchismita Padhy, Anthony Ndirango, Gokce Keskin, Oguz H. Elibol   +7 more
doaj   +3 more sources

Uncertainty weighted multi task learning for robust traffic scene semantic understanding [PDF]

Scientific Reports
This paper addresses perception degradation caused by adverse weather, occlusion, and asynchronous sampling by proposing an uncertainty-weighted multi-task learning framework for robust semantic understanding of traffic scenes (UW-MTL).
Zhiping Wan, Shitong Ye, Feng Wang, Shaojiang Liu, Ling Peng   +4 more
doaj   +2 more sources

Multi-task gradient descent for multi-task learning [PDF]

Memetic Computing, 2020
Multi-Task Learning (MTL) aims to simultaneously solve a group of related learning tasks by leveraging the salutary knowledge memes contained in the multiple tasks to improve the generalization performance. Many prevalent approaches focus on designing a sophisticated cost function, which integrates all the learning tasks and explores the task-task ...
Lu Bai 0005, Yew-Soon Ong, Tiantian He 0001, Abhishek Gupta 0001   +3 more
openaire   +2 more sources

Polymer informatics with multi-task learning [PDF]

Patterns, 2021
Modern data-driven tools are transforming application-specific polymer development cycles. Surrogate models that can be trained to predict the properties of new polymers are becoming commonplace. Nevertheless, these models do not utilize the full breadth of the knowledge available in datasets, which are oftentimes sparse; inherent correlations between ...
Christopher Künneth, Arunkumar Chitteth Rajan, Huan Tran, Lihua Chen, Chiho Kim, Rampi Ramprasad   +5 more
openaire   +3 more sources

Asynchronous Multi-task Learning [PDF]

2016 IEEE 16th International Conference on Data Mining (ICDM), 2016
Many real-world machine learning applications involve several learning tasks which are inter-related. For example, in healthcare domain, we need to learn a predictive model of a certain disease for many hospitals. The models for each hospital may be different because of the inherent differences in the distributions of the patient populations.
Inci M. Baytas, Ming Yan, Anil K. Jain 0001, Jiayu Zhou   +3 more
openaire   +2 more sources

Equitable Multi-task Learning

CoRR, 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.
Jun Yuan, Rui Zhang
openaire   +2 more sources

Hospitalization Patient Forecasting Based on Multi–Task Deep Learning

International Journal of Applied Mathematics and Computer Science, 2023
Forecasting the number of hospitalization patients is important for hospital management. The number of hospitalization patients depends on three types of patients, namely admission patients, discharged patients, and inpatients.
Zhou Min, Huang Xiaoxiao, Liu Haipeng, Zheng Dingchang   +3 more
doaj   +1 more source