Results 71 to 80 of about 7,496 (160)

A Comparative Analysis of Multitask Neural Networks and Stacking Ensemble Learning for Predicting UTS, Weld Hardness, and HAZ Hardness in Welding Applications

Engineering Proceedings
Accurately predicting welding performance measures like ultimate strength (UTS), weld bead hardness, and HAZ mechanical hardness is crucial for ensuring the structural integrity and performance of welded components.
Sama Mukhtar, Amit Sata, Gaurang Joshi, Durgesh Srivastava   +3 more
doaj   +1 more source

On Multiplicative Multitask Feature Learning

, 2016
Advances in Neural Information Processing Systems ...
Wang, Xin, Bi, Jinbo, Yu, Shipeng, Sun, Jiangwen   +3 more
openaire   +2 more sources

TSMAL: Target-Shadow Mask Assistance Learning Network for SAR Target Recognition

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Deep learning-based synthetic aperture radar (SAR) target recognition methods mainly emphasize the amplitude characteristics resulting from backscatter at the target's principal scattering points.
Shuai Guo, Ting Chen, Penghui Wang, Junkun Yan, Hongwei Liu   +4 more
doaj   +1 more source

Ranking by multitask learning

, 2014
Instance ranking is a subfield of supervised machine learning and is concerned with inferring predictive models that can rank a set of data instances. We focus on multipartite ranking, where instances belong to one of a limited set of rank classes, study different approaches on synthetic and real data sets, and propose a ranking-specific evaluation ...
openaire   +3 more sources

“Mirá, mirá [Look at this]”: High school emergent bilingual learners multitasking and collaborating with digital tools

Educational Technology & Society
Technology continually changes day-to-day interactions, and emergent bilingual learners often multitask, using several digital tools, at times simultaneously, to communicate and learn.
Carmen Durham, Loren Jones
doaj   +1 more source

Learning Representation for Multitask Learning Through Self-supervised Auxiliary Learning

Multi-task learning is a popular machine learning approach that enables simultaneous learning of multiple related tasks, improving algorithmic efficiency and effectiveness. In the hard parameter sharing approach, an encoder shared through multiple tasks generates data representations passed to task-specific predictors.
Seokwon Shin, Hyungrok Do, Youngdoo Son
openaire   +2 more sources

Relaxed Equivariance via Multitask Learning

Incorporating equivariance as an inductive bias into deep learning architectures to take advantage of the data symmetry has been successful in multiple applications, such as chemistry and dynamical systems. In particular, roto-translations are crucial for effectively modeling geometric graphs and molecules, where understanding the 3D structures ...
Elhag, Ahmed A., Rusch, T. Konstantin, Di Giovanni, Francesco, Bronstein, Michael   +3 more
openaire   +2 more sources

Learning Task Sampling Policy for Multitask Learning [PDF]

Findings of the Association for Computational Linguistics: EMNLP 2021, 2021
Dhanasekar Sundararaman, Henry Tsai, Kuang-Huei Lee, Iulia Turc, Lawrence Carin   +4 more
openaire   +1 more source

Multiplicative Multitask Feature Learning.

Journal of machine learning research : JMLR
We investigate a general framework of multiplicative multitask feature learning which decomposes individual task's model parameters into a multiplication of two components. One of the components is used across all tasks and the other component is task-specific. Several previous methods can be proved to be special cases of our framework.
Xin, Wang, Jinbo, Bi, Shipeng, Yu, Jiangwen, Sun, Minghu, Song   +4 more
openaire   +1 more source

Multitask Learning-Based Deep Signal Identification for Advanced Spectrum Sensing. [PDF]

Sensors (Basel), 2023
Kim H, Kim YJ, Kim WT.
europepmc   +1 more source