Results 21 to 30 of about 5,803,394 (295)
Media multitasking and implicit learning [PDF]
Attention, Perception, & Psychophysics, 2017 Media multitasking refers to the simultaneous use of different forms of media. Previous research comparing heavy media multitaskers and light media multitaskers suggests that heavy media multitaskers have a broader scope of attention. The present study explored whether these differences in attentional scope would lead to a greater degree of implicit ...
Kathleen S, Edwards, Myoungju, Shin
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Identification of Negative Transfers in Multitask Learning Using Surrogate Models [PDF]
Trans. Mach. Learn. Res., 2023 Multitask learning is widely used in practice to train a low-resource target task by augmenting it with multiple related source tasks. Yet, naively combining all the source tasks with a target task does not always improve the prediction performance for ...
Dongyue Li +2 more
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Embodied Multimodal Multitask Learning [PDF]
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020 Visually-grounded embodied language learning models have recently shown to be effective at learning multiple multimodal tasks such as following navigational instructions and answering questions. In this paper, we address two key limitations of these models, (a) the inability to transfer the grounded knowledge across different tasks and (b) the ...
Chaplot, Devendra Singh +4 more
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Journal of Pathology Informatics, 2022 Background: Prediction of clinical outcomes for individual cancer patients is an important step in the disease diagnosis and subsequently guides the treatment and patient counseling.
Dmitrii Bychkov +12 more
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Bioinspired Architecture Selection for Multitask Learning [PDF]
Frontiers in Neuroinformatics, 2017 Faced with a new concept to learn, our brain does not work in isolation. It uses all previously learned knowledge. In addition, the brain is able to isolate the knowledge that does not benefit us, and to use what is actually useful. In machine learning, we do not usually benefit from the knowledge of other learned tasks. However, there is a methodology
Bueno Crespo, Andrés +3 more
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Entropy, 2020 Different energy systems are closely connected with each other in industrial-park integrated energy system (IES). The energy demand forecasting has important impact on IES dispatching and planning.
Linjuan Zhang +3 more
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Inferring latent task structure for Multitask Learning by Multiple Kernel Learning
BMC Bioinformatics, 2010 Background The lack of sufficient training data is the limiting factor for many Machine Learning applications in Computational Biology. If data is available for several different but related problem domains, Multitask Learning algorithms can be used to ...
Altun Yasemin +3 more
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IEEE Access, 2020 Multitask sparse representation method improves the detection performance by constructing multiple associated sub-sparse representation tasks and jointly learning multiple sub-sparse representation tasks, and this method can make use of the spectral ...
Xianfeng Ou +6 more
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Hierarchical Classification of Insects with Multitask Learning and Anomaly Detection
bioRxiv, 2023 Cameras and computer vision are revolutionising the study of insects, creating new research opportunities within agriculture, epidemiology, evolution, ecology and monitoring of biodiversity.
K. Bjerge +6 more
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UniT: Multimodal Multitask Learning with a Unified Transformer [PDF]
IEEE International Conference on Computer Vision, 2021 We propose UniT, a Unified Transformer model to simultaneously learn the most prominent tasks across different domains, ranging from object detection to natural language understanding and multimodal reasoning.
Ronghang Hu, Amanpreet Singh
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