Results 11 to 20 of about 7,496 (160)
Semina: Ciências Agrárias, 2022 The objective of this study was to evaluate the performance of four machine learning models, as well as multitask learning, to predict soybean root variables from simpler variables, under two water availability conditions.
Anunciene Barbosa Duarte +3 more
doaj +1 more source
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
openaire +2 more sources
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
openaire +2 more sources
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
doaj +3 more sources
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
openaire +4 more sources
Learning Tasks for Multitask Learning [PDF]
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018 © 2018 Copyright held by the owner/author(s). Machine learning approaches have been effective in predicting adverse outcomes in different clinical settings. These models are often developed and evaluated on datasets with heterogeneous patient populations.
Suresh, Harini +2 more
openaire +4 more sources
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
Machine Learning, 1997 Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task can help other tasks be learned better.
openaire +1 more source