3 accepted papers
Irregularly Sampled Medical Time Series (ISMTS) are commonly found in the healthcare domain, where different variables exhibit unique temporal patterns while interrelated. However, many existing methods fail to efficiently consider the differences and correlations among medical variables together, l…
Noisy labels seriously degrade the generalization ability of Deep Neural Networks (DNNs) in various classification tasks. Existing studies on label-noise learning mainly focus on computer vision, while time series also suffer from the same issue. Directly applying the methods from computer vision to…
Semi-supervised learning (SSL) has been actively studied due to its ability to alleviate the reliance of deep learning models on labeled data. Although existing SSL methods based on pseudo-labeling strategies have made great progress, they rarely consider time-series data's intrinsic properties (e.g…