Decentralized federated meta‐learning framework for few‐shot multitask learning
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22951
Abstract: Federated learning is increasingly attractive, however as the number of training samples on a single device is too small and the training tasks of the devices are different, it faces the few‐shot multitask learning problem.… read more here.
Keywords: multitask; shot multitask; decentralized federated; multitask learning ... See more keywords
Multitask Deep Neural Networks for Ames Mutagenicity Prediction
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.2c00532
Abstract: The Ames mutagenicity test constitutes the most frequently used assay to estimate the mutagenic potential of drug candidates. While this test employs experimental results using various strains of Salmonella typhimurium, the vast majority of the… read more here.
Keywords: neural networks; multitask; ames mutagenicity; deep neural ... See more keywords
Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space
Sign Up to like & getrecommendations! Published in 2019 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.8b00685
Abstract: Acute toxicity is one of the most challenging properties to predict purely with computational methods due to its direct relationship to biological interactions. Moreover, toxicity can be represented by different end points: it can be… read more here.
Keywords: broad chemical; multitask; comparative study; toxicity ... See more keywords
Prediction of Compound Profiling Matrices, Part II: Relative Performance of Multitask Deep Learning and Random Forest Classification on the Basis of Varying Amounts of Training Data
Sign Up to like & getrecommendations! Published in 2018 at "ACS Omega"
DOI: 10.1021/acsomega.8b01682
Abstract: Currently, there is a high level of interest in deep learning and multitask learning in many scientific fields including the life sciences and chemistry. Herein, we investigate the performance of multitask deep neural networks (MT-DNNs)… read more here.
Keywords: training data; multitask; prediction performance; performance ... See more keywords
Robust Multitask Diffusion Affine Projection Algorithm for Distributed Estimation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Circuits and Systems II: Express Briefs"
DOI: 10.1109/tcsii.2021.3103868
Abstract: When the disturbance of impulsive noise exists in the multitask network, the convergence behavior of the traditional multitask diffusion affine projection (AP) algorithm (MD-APA) is significantly suppressed. To alleviate this problem, in this brief, a… read more here.
Keywords: affine projection; multitask diffusion; estimation; apmcc algorithm ... See more keywords
Robust Visual Tracking With Multitask Joint Dictionary Learning
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2016.2515738
Abstract: Dictionary learning for sparse representation has been increasingly applied to object tracking, however, the existing methods only utilize one modality of the object to learn a single dictionary. In this paper, we propose a robust… read more here.
Keywords: joint dictionary; multitask; visual tracking; dictionary learning ... See more keywords
BMT-Net: Broad Multitask Transformer Network for Sentiment Analysis.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2021.3050508
Abstract: Sentiment analysis uses a series of automated cognitive methods to determine the author's or speaker's attitudes toward an expressed object or text's overall emotional tendencies. In recent years, the growing scale of opinionated text from… read more here.
Keywords: representation; sentiment; multitask; sentiment analysis ... See more keywords
A Deep Multitask Learning Framework Coupling Semantic Segmentation and Fully Convolutional LSTM Networks for Urban Change Detection
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2021.3055584
Abstract: In this article, we present a deep multitask learning framework able to couple semantic segmentation and change detection using fully convolutional long short-term memory (LSTM) networks. In particular, we present a UNet-like architecture (L-UNet) that… read more here.
Keywords: fully convolutional; multitask; change detection; semantic segmentation ... See more keywords
Application of Multitask Learning for 2-D Modeling of Magnetotelluric Surveys: TE Case
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2021.3101119
Abstract: In this article, multitask learning is applied to forward modeling of 2-D magnetotellurics (MT) to predict the apparent resistivity and impedance phase of MT data. Multitask learning can learn multiple objectives simultaneously based on the… read more here.
Keywords: apparent resistivity; multitask; resistivity impedance; multitask learning ... See more keywords
Multitask Deep Learning Reconstruction and Localization of Lesions in Limited Angle Diffuse Optical Tomography
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Medical Imaging"
DOI: 10.1109/tmi.2021.3117276
Abstract: Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to assess its optical properties and identify abnormalities. DOT image reconstruction is an ill-posed problem due to the highly scattered photons in the medium and… read more here.
Keywords: reconstruction; diffuse optical; multitask; deep learning ... See more keywords
End-to-End Multitask Learning With Vision Transformer.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2023.3234166
Abstract: Multitask learning (MTL) is a challenging puzzle, particularly in the realm of computer vision (CV). Setting up vanilla deep MTL requires either hard or soft parameter sharing schemes that employ greedy search to find the… read more here.
Keywords: multitask; multitask learning; task; end ... See more keywords