Combining Model-Agnostic Meta-Learning and Transfer Learning for Regression [PDF]
Sensors, 2023 For cases in which a machine learning model needs to be adapted to a new task, various approaches have been developed, including model-agnostic meta-learning (MAML) and transfer learning.
Wahyu Fadli Satrya, Ji-Hoon Yun
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Ensemble-Based Model-Agnostic Meta-Learning with Operational Grouping for Intelligent Sensory Systems [PDF]
SensorsModel-agnostic meta-learning (MAML), coupled with digital twins, is transformative for predictive maintenance (PdM), especially in robotic arms in assembly lines, where rapid and accurate fault classification of arms is essential.
Mainak Mallick +3 more
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Few-shot crop disease recognition using sequence- weighted ensemble model-agnostic meta-learning [PDF]
Frontiers in Plant ScienceDiseases pose significant threats to crop production, leading to substantial yield reductions and jeopardizing global food security. Timely and accurate detection of crop diseases is essential for ensuring sustainable agricultural development and ...
Junlong Li +5 more
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Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning [PDF]
International Conference on Machine Learning, 2022 Model-agnostic meta learning (MAML) is currently one of the dominating approaches for few-shot meta-learning. Albeit its effectiveness, the optimization of MAML can be challenging due to the innate bilevel problem structure.
Momin Abbas +4 more
semanticscholar +3 more sources
An Adaptive Framework for Intrusion Detection in IoT Security Using MAML (Model-Agnostic Meta-Learning). [PDF]
Sensors (Basel)With the rapid emergence of the Internet of Things (IoT) devices, there were new vectors for attacking cyber, so there was a need for approachable intrusion detection systems (IDSs) with more innovative custom tactics.
Alrayes FS, Amin SU, Hakami N.
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Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection [PDF]
IEEE Transactions on Computational Social Systems, 2023 Hate speech in social media is a growing phenomenon, and detecting such toxic content has recently gained significant traction in the research community.
Rabiul Awal +4 more
semanticscholar +3 more sources
Few-Shot Learning for Medical Image Segmentation Using 3D U-Net and Model-Agnostic Meta-Learning (MAML) [PDF]
Diagnostics (Basel)Deep learning has attained state-of-the-art results in general image segmentation problems; however, it requires a substantial number of annotated images to achieve the desired outcomes. In the medical field, the availability of annotated images is often
Alsaleh A +4 more
europepmc +2 more sources
TCN-MAML: A TCN-Based Model with Model-Agnostic Meta-Learning for Cross-Subject Human Activity Recognition. [PDF]
Sensors (Basel)Human activity recognition (HAR) using Wi-Fi-based sensing has emerged as a powerful, non-intrusive solution for monitoring human behavior in smart environments. Unlike wearable sensor systems that require user compliance, Wi-Fi channel state information
Lin CY +5 more
europepmc +2 more sources
A Semi-Supervised Adversarial Robust Model-Agnostic Meta-Learning Method [PDF]
Jisuanji gongcheng, 2022 The meta model developed by meta learning is expected to have the ability of "learning to learn". Therefore, it can quickly adapt to new tasks based on the learned "meta knowledge", with a small number of gradient descent steps to ...
HU Bin, WANG Xiaojun, ZHANG Lei
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A Model-Agnostic Meta-Baseline Method for Few-Shot Fault Diagnosis of Wind Turbines
Sensors, 2022 The technology of fault diagnosis is helpful to improve the reliability of wind turbines, and further reduce the operation and maintenance cost at wind farms. However, in reality, wind turbines are not allowed to operate with faults, so few fault samples
Xiaobo Liu, Wei Teng, Yibing Liu
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