In a Nature Communications study, researchers from China have developed an error-aware probabilistic update (EaPU) method ...
Background The global surge in ultra-processed food (UPF) consumption is a major public health challenge, particularly among ...
LLM-Based Data Augmentation Method in Reinforcement Learning With Machine-Unlearning and Fine-Tuning
Abstract: Data augmentation in reinforcement learning (RL) aims to generate diverse and extensive datasets to enhance the learning process. Most existing studies on RL augmentation employ sample-based ...
ABSTRACT: A degenerative neurological condition called Parkinson disease (PD) that evolves progressively, making detection difficult. A neurologist requires a clear healthcare history from the ...
Time series classification is widely used in many fields, but it often suffers from a lack of labeled data. To address this, researchers commonly apply data augmentation techniques that generate ...
Abstract: Federated learning is an important distributed machine learning paradigm. This study proposes a privacy-preserving data augmentation model for federated learning of heterogeneous data, which ...
1 Prairie View A&M University, Electrical and Computer Engineering, Texas A&M University System, Prairie View, TX, United States 2 Texas Juvenile Crime Prevention Center, Prairie View A&M University, ...
This repository contains code for augmenting, training, and analyzing heart-sound classification models using the PCGmix method. models.py: Deep learning models for 1D time-series classification, ...
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