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Multi-task dynamical systems: customising time series models [PDF]
, 2021 Time series datasets are usually composed of a variety of sequences from the same domain, but from different entities, such as individuals, products, or organizations.
Bird, Alex
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Towards Improving Generalization of Multi-Task Learning
, 2021 Multi-task Learning (MTL), which involves the simultaneous learning of multiple tasks, can achieve better performance than learning each task independently.
Mao, Yuren
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Abusive Content Detection in Arabic Tweets Using Multi-Task Learning and Transformer-Based Models
Applied Sciences, 2023 Different social media platforms have become increasingly popular in the Arab world in recent years. The increasing use of social media, however, has also led to the emergence of a new challenge in the form of abusive content, including hate speech ...
Bedour Alrashidi +2 more
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An overview of multi-task learning
, 2017 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.
Qiang Yang, Yu Zhang
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Towards Impartial Multi-task Learning [PDF]
, 2021 Multi-task learning (MTL) has been widely used in representation learning. However, naively training all tasks simultaneously may lead to the partial training issue, where specific tasks are trained more adequately than others.
Kuang, Z +7 more
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Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL
CoRR, 2012 Since its inception, the modus operandi of multi-task learning (MTL) has been to minimize the task-wise mean of the empirical risks. We introduce a generalized loss-compositional paradigm for MTL that includes a spectrum of formulations as a subfamily. One endpoint of this spectrum is minimax MTL: a new MTL formulation that minimizes the maximum of the
Nishant A. Mehta +2 more
openaire +3 more sources
Multi-Task Learning Model Based on Multi-Scale CNN and LSTM for Sentiment Classification
IEEE Access, 2020 Sentiment classification is an interesting and crucial research topic in the field of natural language processing (NLP). Data-driven methods, including machine learning and deep learning techniques, provide one direct and effective solution to solve the ...
Ning Jin +4 more
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PLoS Computational Biology, 2023 Many complex diseases share common genetic determinants and are comorbid in a population. We hypothesized that the co-occurrences of diseases and their overlapping genetic etiology can be exploited to simultaneously improve multiple diseases' polygenic ...
Adrien Badré, Chongle Pan
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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 +7 more
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MTFormer: Multi-task Learning via Transformer and Cross-Task Reasoning
, 2022 In this paper, we explore the advantages of utilizing transformer structures for addressing multi-task learning (MTL). Specifically, we demonstrate that models with transformer structures are more appropriate for MTL than convolutional neural networks ...
Zhao, HS +9 more
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