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   Articles with "multitask" as a keyword



Decentralized federated meta‐learning framework for few‐shot multitask learning

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recommendations! 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

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recommendations! 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

Highly Accurate and Explainable Predictions of Small-Molecule Antioxidants for Eight In Vitro Assays Simultaneously through an Alternating Multitask Learning Strategy

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recommendations! Published in 2024 at "Journal of chemical information and modeling"

DOI: 10.1021/acs.jcim.4c00748

Abstract: Small molecule antioxidants can inhibit or retard oxidation reactions and protect against free radical damage to cells, thus playing a key role in food, cosmetics, pharmaceuticals, the environment, as well as materials. Experimentally driven antioxidant… read more here.

Keywords: molecule; molecule antioxidants; multitask; alternating multitask ... See more keywords

Data Fusion of Deep Learned Molecular Embeddings for Property Prediction

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recommendations! Published in 2025 at "Journal of chemical information and modeling"

DOI: 10.1021/acs.jcim.5c01728

Abstract: Data-driven approaches such as deep learning can result in predictive models for material properties with exceptional accuracy and efficiency. However, in many applications, data is sparse, severely limiting their accuracy and applicability. To improve predictions,… read more here.

Keywords: chemistry; multitask; data fusion; deep learned ... See more keywords

Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space

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recommendations! 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

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recommendations! 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

Transcriptome Transformer: improving patient survival prediction via multitask learning of transcriptomic and clinical features

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recommendations! Published in 2025 at "Briefings in Bioinformatics"

DOI: 10.1093/bib/bbaf628

Abstract: Abstract Accurate survival prediction is essential in healthcare as it guides treatment strategies and improves patient outcomes. While clinical features provide valuable prognostic information, they often fail to represent the molecular complexity of diseases. Transcriptomic… read more here.

Keywords: multitask; clinical features; prediction; transcriptome transformer ... See more keywords

Smart Fiber-Optic Distributed Acoustic Sensing (sDAS) With Multitask Learning for Time-Efficient Ground Listening Applications

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recommendations! Published in 2024 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2023.3320149

Abstract: In recent years, fiber-optical distributed acoustic sensing (DAS) has been applied to various large-scale infrastructure monitoring areas in smart cities, leading to a new generation of fiber-optic IoT for ground listening. However, its single-task-focused postprocessing… read more here.

Keywords: time; multitask; ground; fiber ... See more keywords

MTSR-GAN: A progressive residual GAN structure combined with the Swin-Transformer for multitask cross-sensor satellite image super-resolution

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recommendations! Published in 2025 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"

DOI: 10.1109/jstars.2025.3631332

Abstract: In recent years, super-resolution (SR) reconstruction techniques for remote sensing imagery has attracted extensive attention due to its advantages of a low cost and flexible and convenient application. Unlike conventional SR tasks, the SR reconstruction… read more here.

Keywords: swin transformer; cross sensor; multitask; reconstruction ... See more keywords

Robust Multitask Diffusion Affine Projection Algorithm for Distributed Estimation

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recommendations! 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

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recommendations! 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