Results 41 to 50 of about 603,409 (311)
Multi-Task Learning Based Network Embedding
Frontiers in Neuroscience, 2020 The goal of network representation learning, also called network embedding, is to encode the network structure information into a continuous low-dimensionality embedding space where geometric relationships among the vectors can reflect the relationships ...
Shanfeng Wang, Qixiang Wang, Maoguo Gong
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Resource-Efficient Multi-Task Deep Learning Using a Multi-Path Network
IEEE Access, 2022 Multi-task learning (MTL) improves learning efficiency compared to the single-task counterpart in that it performs multiple tasks at the same time. Due to the nature, it can achieve generalized performance as well as alleviate overfitting.
Soyeon Park, Jiho Lee, Eunwoo Kim
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CoRR, 2017 Federated learning poses new statistical and systems challenges in training machine learning models over distributed networks of devices. In this work, we show that multi-task learning is naturally suited to handle the statistical challenges of this setting, and propose a novel systems-aware optimization method, MOCHA, that is robust to practical ...
Virginia Smith +3 more
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Convex multi-task feature learning [PDF]
Machine Learning, 2007 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Andreas Argyriou +2 more
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Multi-Task Learning with Multi-Task Optimization
CoRRMulti-task learning solves multiple correlated tasks. However, conflicts may exist between them. In such circumstances, a single solution can rarely optimize all the tasks, leading to performance trade-offs. To arrive at a set of optimized yet well-distributed models that collectively embody different trade-offs in one algorithmic pass, this paper ...
Lu Bai 0005 +2 more
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Networked Federated Multi-Task Learning [PDF]
, 2021 we characterize the network structure of data such that federated mulit-task learning is possible. <br>
Yasmin SarcheshmehPour +3 more
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Multi-task semi-supervised adversarial autoencoding for speech emotion recognition [PDF]
, 2020 Inspite the emerging importance of Speech Emotion Recognition (SER), the state-of-the-art accuracy is quite low and needs improvement to make commercial applications of SER viable.
Latif, Siddique +6 more
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Multi-task learning for pKa prediction [PDF]
Journal of Computer-Aided Molecular Design, 2012 Journal of Computer-Aided Molecular Design, 26 (7)
Grigorios Skolidis +3 more
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Multi-Task Network Anomaly Detection using Federated Learning
, 2019 Because of the complexity of network traffic, there are various significant challenges in the network anomaly detection fields. One of the major challenges is the lack of labeled training data.
Jian Teng +9 more
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Sharing Knowledge in Multi-Task Deep Reinforcement Learning [PDF]
, 2020 We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning. We leverage the assumption that learning from different tasks, sharing common properties, is helpful to ...
A. Bonarini +9 more
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