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Uncertainty weighted multi task learning for robust traffic scene semantic understanding [PDF]
Scientific ReportsThis 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 +4 more
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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 +3 more
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Correlation learning based multi-task model and its application
Shenzhen Daxue xuebao. Ligong ban, 2023 Multi-task learning is a means of learning by combining multiple tasks simultaneously to enhance the model representation and generalization ability. The correlation between tasks is the key factor for the construction of multi-task learning model.
XU Wei +3 more
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Prediction of drug–target interactions through multi-task learning
Scientific Reports, 2022 Identifying the binding between the target proteins and molecules is essential in drug discovery. The multi-task learning method has been introduced to facilitate knowledge sharing among tasks when the amount of information for each task is small ...
Chaeyoung Moon, Dongsup Kim
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Multi-Task Deep Learning Games: Investigating Nash Equilibria and Convergence Properties
Axioms, 2023 This paper conducts a rigorous game-theoretic analysis on multi-task deep learning, providing mathematical insights into the dynamics and interactions of tasks within these models.
Minhyeok Lee
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Multi-Task Transformer with Adaptive Cross-Entropy Loss for Multi-Dialect Speech Recognition
Entropy, 2022 At present, most multi-dialect speech recognition models are based on a hard-parameter-sharing multi-task structure, which makes it difficult to reveal how one task contributes to others.
Zhengjia Dan +4 more
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FishNet: Fish visual recognition with one stage multi‐task learning
IET Image Processing, 2022 The use of computer vision for fish monitoring in aquaculture fisheries has gained importance. It is crucial to obtain the object box, instance mask and landmarks of the fish to determine their status.
Ziwen Chen +3 more
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Task-Aware Dynamic Model Optimization for Multi-Task Learning
IEEE Access, 2023 Multi-task learning (MTL) is a field in which a deep neural network simultaneously learns knowledge from multiple tasks. However, achieving resource-efficient MTL remains challenging due to entangled network parameters across tasks and varying task ...
Sujin Choi, Hyundong Jin, Eunwoo Kim
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Multi-Task Network Representation Learning
Frontiers in Neuroscience, 2020 Networks, such as social networks, biochemical networks, and protein-protein interaction networks are ubiquitous in the real world. Network representation learning aims to embed nodes in a network as low-dimensional, dense, real-valued vectors, and ...
Yu Xie +4 more
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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
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