4 citations · 6 across the 8 of their papers we have counts for
6 papers · 1 filter
Principal Components for Neural Network Initialization
Nhan Phan, Thu Nguyen, Uyen Dang +2
Principal Component Analysis (PCA) is a commonly used tool for dimension reduction and denoising. Therefore, it is also widely used on the data prior to training a neural network.…
Missing data imputation for noisy time-series data and applications in healthcare
Lien P. Le, Xuan-Hien Nguyen Thi, Thu Nguyen +3
Healthcare time series data is vital for monitoring patient activity but often contains noise and missing values due to various reasons such as sensor errors or data interruptions.…
Explainability of Machine Learning Models under Missing Data
Tuan L. Vo, Thu Nguyen, Luis M. Lopez-Ramos +3
Missing data is a prevalent issue that can significantly impair model performance and explainability. This paper briefly summarizes the development of the field of missing data wit…
Advancing sleep detection by modelling weak label sets: A novel weakly supervised learning approach
Matthias Boeker, Vajira Thambawita, Michael Riegler +2
Understanding sleep and activity patterns plays a crucial role in physical and mental health. This study introduces a novel approach for sleep detection using weakly supervised lea…
Imputation using training labels and classification via label imputation
Thu Nguyen, Tuan L. Vo, Pål Halvorsen +1
Missing data is a common problem in practical data science settings. Various imputation methods have been developed to deal with missing data. However, even though the labels are a…
Correlation visualization under missing values: a comparison between imputation and direct parameter estimation methods
Nhat-Hao Pham, Khanh-Linh Vo, Mai Anh Vu +4
Correlation matrix visualization is essential for understanding the relationships between variables in a dataset, but missing data can pose a significant challenge in estimating co…