Results 21 to 30 of about 7,496 (160)
Distilling Knowledge with a Teacher’s Multitask Model for Biomedical Named Entity Recognition
Information, 2023 Single-task models (STMs) struggle to learn sophisticated representations from a finite set of annotated data. Multitask learning approaches overcome these constraints by simultaneously training various associated tasks, thereby learning generic ...
Tahir Mehmood +4 more
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Parallel learning by multitasking neural networks
Journal of Statistical Mechanics: Theory and Experiment, 2023 Abstract Parallel learning, namely the simultaneous learning of multiple patterns, constitutes a modern challenge for neural networks. While this cannot be accomplished by standard Hebbian associative neural networks, in this paper we show how the multitasking Hebbian network (a variation on the theme of the Hopfield ...
Agliari E. +3 more
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Bayesian Multitask Inverse Reinforcement Learning [PDF]
, 2012 We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or as different experts trying to solve the same task. Our main contribution is to formalise the problem as statistical preference elicitation, via a number of structured priors,
Dimitrakakis C., Rothkopf C.A.
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Unsupervised online multitask learning of behavioral sentence embeddings [PDF]
PeerJ Computer Science, 2019 Appropriate embedding transformation of sentences can aid in downstream tasks such as NLP and emotion and behavior analysis. Such efforts evolved from word vectors which were trained in an unsupervised manner using large-scale corpora.
Shao-Yen Tseng +2 more
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IEEE Access, 2021 This paper proposes a data-driven, condition-based maintenance framework (DCBM) for deteriorating equipment under the impact of varying environments and natural aging. The equipment's degradation status is determined by a prognostic and health monitoring
Lei Zhang, Jianguo Zhang
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Low-Rank Representation-Based Object Tracking Using Multitask Feature Learning with Joint Sparsity
Abstract and Applied Analysis, 2014 We address object tracking problem as a multitask feature learning process based on low-rank representation of features with joint sparsity. We first select features with low-rank representation within a number of initial frames to obtain subspace basis.
Hyuncheol Kim, Joonki Paik
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LAND USE CLASSIFICATION USING DEEP MULTITASK NETWORKS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Updated information on urban land use allows city planners and decision makers to conduct large scale monitoring of urban areas for sustainable urban growth.
J. R. Bergado, C. Persello, A. Stein
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Multitask Siamese Network for Remote Photoplethysmography and Respiration Estimation
Sensors, 2022 Heart and respiration rates represent important vital signs for the assessment of a person’s health condition. To estimate these vital signs accurately, we propose a multitask Siamese network model (MTS) that combines the advantages of the Siamese ...
Heejin Lee +6 more
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Probabilistic Low-Rank Multitask Learning
IEEE Transactions on Neural Networks and Learning Systems, 2018 In this paper, we consider the problem of learning multiple related tasks simultaneously with the goal of improving the generalization performance of individual tasks. The key challenge is to effectively exploit the shared information across multiple tasks as well as preserve the discriminative information for each individual task.
Yu Kong, Ming Shao, Kang Li, Yun Fu
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Multitask learning for blackmarket tweet detection [PDF]
Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2019 4 pages, IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM ...
Arora, Udit +2 more
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