Results 81 to 90 of about 5,803,394 (295)

A Multitask Learning-Based Vision Transformer for Plant Disease Localization and Classification

International Journal of Computational Intelligence Systems
Plant disease detection is a critical task in agriculture, essential for ensuring crop health and productivity. Traditional methods in this context are often labor-intensive and prone to errors, highlighting the need for automated solutions.
S. Hemalatha, Jai Jaganath, Babu Jayachandran   +2 more
semanticscholar   +1 more source

Harnessing Large Language Models to Advance Microbiome Research: From Sequence Analysis to Clinical Applications

Advanced Intelligent Discovery, EarlyView.
Large language models are transforming microbiome research by enabling advanced sequence profiling, functional prediction, and association mining across complex datasets. They automate microbial classification and disease‐state recognition, improving cross‐study integration and clinical diagnostics.
Jieqi Xing, He Wang, Yangyang Sun, Xiaoquan Su, Shunyao Wu   +4 more
wiley   +1 more source

Adversarial Multitask Learning for Domain Adaptation Through Domain Adapter

IEEE Access
This study presents a technique called Adversarial Multitask Learning (AML) to enhance the effectiveness of domain adaptation methods in practical applications, which are currently highly sought after. The proposed approach addresses the challenges posed
Hidayaturrahman, Agung Trisetyarso, Iman Herwidiana Kartowisastro, Widodo Budiharto   +3 more
doaj   +1 more source

Multitask Soft Option Learning

, 2019
Published at UAI ...
Igl, Maximilian (author), Gambardella, Andrew (author), He, J. (author), Nardelli, Nantas (author), Siddharth, N (author), Böhmer, J.W. (author), Whiteson, Shimon (author)   +6 more
openaire   +3 more sources

Bayesian Optimization Guiding the Experimental Mapping of the Pareto Front of Mechanical and Flame‐Retardant Properties in Polyamide Nanocomposites

Advanced Intelligent Discovery, EarlyView.
Bayesian optimization enabled the design of PA56 system with just 8 wt% additives, achieving limiting oxygen index 30.5%, tensile strength 80.9 MPa, and UL‐94 V‐0 rating. Without prior knowledge, the algorithm uncovered synergistic effects between aluminum diethyl‐phosphinate and nanoclay.
Burcu Ozdemir, Miguel Hernández‐del‐Valle, Christina Schenk, De‐Yi Wang, Maciej Haranczyk   +4 more
wiley   +1 more source

Multitask learning without label correspondences [PDF]

, 2011
We propose an algorithm to perform multitask learning where each task has potentially distinct label sets and label correspondences are not readily available.
Caetano, Tiberio, Petterson, James, Quadrianto, Novi, Smola, Alexander, Vishwanathan, S V N   +4 more
core   +1 more source

Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

, 2019
The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle ...
A Kumar, B Liu, D Schoch, DJ Watts, E Estrada, F Grando, F Scarselli, G Sethuraman, J Kim, M Beauchamp, M Shaw, P Bonacich, P Holme, R Albert, SP Borgatti, U Brandes, V Batagelj   +16 more
core   +1 more source

A Cost-Sensitive Machine Learning Model With Multitask Learning for Intrusion Detection in IoT

IEEE Transactions on Industrial Informatics
A problem with machine learning (ML) techniques for detecting intrusions in the Internet of Things (IoT) is that they are ineffective in the detection of low-frequency intrusions.
A. Telikani, Nima Esmi Rudbardeh, Shiva Soleymanpour, A. Shahbahrami, Jun Shen, Georgi Gaydadjiev, Reza Hassanpour   +6 more
semanticscholar   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Hierarchical Multitask Learning With CTC [PDF]

2018 IEEE Spoken Language Technology Workshop (SLT), 2018
In Automatic Speech Recognition, it is still challenging to learn useful intermediate representations when using high-level (or abstract) target units such as words. For that reason, when only a few hundreds of hours of training data are available, character or phoneme-based systems tend to outperform word-based systems.
Ramon Sanabria, Florian Metze
openaire   +1 more source