Multi-Variable Transformer-Based Meta-Learning for Few-Shot Fault Diagnosis of Large-Scale Systems. [PDF]
Sensors (Basel)Li W, Nie Y, Yang F.
europepmc +1 more source
Does Camouflaging Predict Functioning, Distress, and Quality of Life for Autistic Adults?
Autism Research, EarlyView.ABSTRACT It has been proposed that autistic individuals adopt camouflaging strategies to mask their autistic traits and conform to social norms, and that these camouflaging strategies have been linked to adverse mental health outcomes. This study examined whether camouflaging, measured by the Camouflaging Autistic Traits Questionnaire (CAT‐Q ...
Bruna B. Roisenberg +3 more
wiley +1 more source
An Adaptive Framework for Intrusion Detection in IoT Security Using MAML (Model-Agnostic Meta-Learning). [PDF]
Sensors (Basel)Alrayes FS, Amin SU, Hakami N.
europepmc +1 more source
Ensemble-Based Model-Agnostic Meta-Learning with Operational Grouping for Intelligent Sensory Systems. [PDF]
Sensors (Basel)Mallick M, Shim YD, Won HI, Choi SK.
europepmc +1 more source
More than words: The value of qualitative research for advancing educational practice
Anatomical Sciences Education, EarlyView.
Georgina C. Stephens +1 more
wiley +1 more source
Personalized blood glucose prediction in type 1 diabetes using meta-learning with bidirectional long short term memory-transformer hybrid model. [PDF]
Sci RepMoon K, Kim J, Yoo S, Cho J.
europepmc +1 more source
Publisher Correction: Improving landslide susceptibility prediction through ensemble recursive feature elimination and meta-learning framework. [PDF]
Sci RepHalder K +9 more
europepmc +1 more source
Reassessing deep learning (and meta-learning) computer vision as an efficient method to determine taphonomic agency in bone surface modifications. [PDF]
Biol Methods ProtocDomínguez-Rodrigo M +3 more
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Few-shot hotel industry site selection prediction method based on meta learning algorithms and transportation accessibility. [PDF]
Sci RepLi N, Wu H.
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Swarm learning is a kind of decentralized machine learning. In this paper, we propose a new framework of decentralized collaborative learning, called swarm meta learning, by combining swarm learning with meta learning, blockchain, and federated learning.
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