Results 21 to 30 of about 603,409 (311)
Multi-task learning with summary statistics
Advances in Neural Information Processing Systems 36, 2023 Multi-task learning has emerged as a powerful machine learning paradigm for integrating data from multiple sources, leveraging similarities between tasks to improve overall model performance. However, the application of multi-task learning to real-world settings is hindered by data-sharing constraints, especially in healthcare settings. To address this
Parker Knight, Rui Duan
openaire +4 more sources
Multi-Task Learning Based on Stochastic Configuration Networks
Frontiers in Bioengineering and Biotechnology, 2022 When the human brain learns multiple related or continuous tasks, it will produce knowledge sharing and transfer. Thus, fast and effective task learning can be realized. This idea leads to multi-task learning.
Xue-Mei Dong +2 more
doaj +1 more source
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.
Yaqi Duan, Kaizheng Wang
openaire +3 more sources
Learning Gait Representations with Noisy Multi-Task Learning
Sensors, 2022 Gait analysis is proven to be a reliable way to perform person identification without relying on subject cooperation. Walking is a biometric that does not significantly change in short periods of time and can be regarded as unique to each person. So far,
Adrian Cosma, Emilian Radoi
doaj +1 more source
Improving Persian Named Entity Recognition Through Multi Task Learning
International Journal of Information and Communication Technology Research, 2021 Named Entity Recognition is a challenging task, specially for low resource languages, such as Persian, due to the lack of massive gold data. As developing manually-annotated datasets is time consuming and expensive, we use a multitask learning (MTL ...
Mohammad Hadi Bokaei +2 more
doaj
Multi‐task learning for captioning images with novel words
IET Computer Vision, 2019 Recent captioning models are limited in their ability to describe concepts unseen in paired image–sentence pairs. This study presents a framework of multi‐task learning for describing novel words not present in existing image‐captioning datasets.
He Zheng +4 more
doaj +1 more source
Sign-Regularized Multi-Task Learning
, 2023 17 pages, 4 figures ...
Johnny Torres +5 more
openaire +2 more sources
Pareto Multi-task Deep Learning [PDF]
, 2020 Neuroevolution has been used to train Deep Neural Networks on reinforcement learning problems. A few attempts have been made to extend it to address either multi-task or multi-objective optimization problems. This research work presents the Multi-Task Multi-Objective Deep Neuroevolution method, a highly parallelizable algorithm that can be adopted for ...
Riccio S. D. +4 more
openaire +2 more sources
Learning in multi-agent systems [PDF]
, 2001 In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as ...
Alonso, E. +14 more
core +1 more source