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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Dif-MAML: Decentralized Multi-Agent Meta-Learning [PDF]
IEEE Open Journal of Signal Processing, 2022 The objective of meta-learning is to exploit knowledge obtained from observed tasks to improve adaptation to unseen tasks. Meta-learners are able to generalize better when they are trained with a larger number of observed tasks and with a larger amount ...
Mert Kayaalp, Stefan Vlaski, Ali Sayed
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Bearing Fault Diagnosis Based on Small Sample Learning of Maml–Triplet
Applied Sciences, 2022 Since the emergence of artificial intelligence and deep learning methods, the fault diagnosis of bearings in rotating machinery has gradually been realized, reducing the high costs of bearing faults.
Qiang Cheng +5 more
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Alpha MAML: Adaptive Model-Agnostic Meta-Learning
, 2019 Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks.
Baydin, Atılım Güneş +2 more
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Meta-learning provides a robust framework to discern taxonomic carnivore agency from the analysis of tooth marks on bone: reassessing the role of felids as predators of Homo habilis [PDF]
Royal Society Open ScienceDetermining carnivore agency in taphonomic research is crucial for identifying site formation processes and carnivore–hominin interactions that influenced human evolution.
Manuel Domínguez-Rodrigo +4 more
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Cellular Distribution and Motion of Essential Magnetosome Proteins Expressed in Mammalian Cells [PDF]
BiosensorsMagnetosomes are organelle-like structures within magnetotactic bacteria that store iron biominerals in membrane-bound vesicles. In bacteria, formation of these structures is highly regulated by approximately 30 genes, which are conserved throughout ...
Qin Sun +5 more
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Eternal-MAML: a meta-learning framework for cross-domain defect recognition [PDF]
PeerJ Computer ScienceDefect recognition tasks for industrial product suffer from a serious lack of samples, greatly limiting the generalizability of deep learning models. Addressing the imbalance of defective samples often involves leveraging pre-trained models for transfer ...
Jipeng Feng, Haigang Zhang, Zhifeng Wang
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A lightweight deep neural network for personalized detecting ventricular arrhythmias from a single-lead ECG device. [PDF]
PLOS Digital HealthVentricular arrhythmia (VA) is a leading cause of sudden cardiac death. Detecting VA from electrocardiograms (ECGs) using deep learning techniques has potential to improve clinical outcomes.
Zhejun Sun +11 more
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Adaptive weighted dual MAML: Proposing a novel method for the automated diagnosis of partial sleep deprivation. [PDF]
PLoS ONEIntroductionSleep disorders significantly disrupt normal sleep patterns and pose serious health risks. Traditional diagnostic methods, such as questionnaires and polysomnography, often require extensive time and are susceptible to errors. This highlights
Soraya Khanmohmmadi +4 more
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TCN-MAML: A TCN-Based Model with Model-Agnostic Meta-Learning for Cross-Subject Human Activity Recognition [PDF]
SensorsHuman 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
Chih-Yang Lin +5 more
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