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Lightweight Multimodal Fusion for Urban Tree Health and Ecosystem Services. [PDF]
Sensors (Basel)Buriboev A +7 more
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MTL-LSTM: Multi-Task Learning-based LSTM for Urban Traffic Flow Forecasting
2021 International Wireless Communications and Mobile Computing (IWCMC), 2021 Predicting traffic flow in large cities is beneficial for a wide range of applications, including vehicle navigation services, vehicle routing, and traffic congestion management. In this scenario, deep learning approaches such as Recurrent Neural Networks (RNN) and its variant Long Short Term Memory (LSTM) are excellent alternatives due to their ...
Mostafa Karimzadeh +4 more
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GA-MTL: A Random Method of Multi-Task Learning
The Proceedings of the Multiconference on "Computational Engineering in Systems Applications", 2006Multi-task learning techniques can employ the removed redundant information to improve prediction accuracy. Which features to add to the target and/or the input during multi-task learning is still an open issue. The previous study used heuristic search methods.
T.-Y. Liu, G.-Z. Li, G.-F. Wu, E. C. Chi
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Usr-mtl: an unsupervised sentence representation learning framework with multi-task learning
Applied Intelligence, 2020Developing the utilized intelligent systems is increasingly important to learn effective text representations, especially extract the sentence features. Numerous previous studies have been concentrated on the task of sentence representation learning based on deep learning approaches.
Shuangyin Li, Yonghe Lu, Lu Yonghe
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A Survey on Multi-Task Learning
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.
Yu Zhang, Yang Qiang
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MTL-Leak: Privacy Risk Assessment in Multi-Task Learning
IEEE Transactions on Dependable and Secure ComputingHongyang Yan, Anli Yan, Li Hu
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MTL-FoUn: A Multi-Task Learning Approach to Form Understanding
2021Form layout understanding is a task of extracting and structuring information from scanned documents, and consists of primarily three tasks: (i) word grouping, (ii) entity labeling and (iii) entity linking. While the three tasks are dependent on each other, current approaches have solved each of these problems independently.
Nishant Prabhu +2 more
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MTL-SIMNAS: Task Similarity-Driven Neural Architecture Search for Enhanced Multi-task Learning
Lecture Notes in Computer ScienceMathias Verbeke
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Comic MTL: optimized multi-task learning for comic book image analysis
International Journal on Document Analysis and Recognition (IJDAR), 2019Comic book image analysis methods often propose multiple algorithms or models for multiple tasks like panel and character (body and face) detection, balloon segmentation, text recognition, etc. In this work, we aim to reduce the processing time for comic book image analysis by proposing one model that can learn multiple tasks called Comic MTL instead ...
Nhu-Van Nguyen +2 more
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