Changjian Chen

Tailoring data augmentation to individual learning states boosts performance by an average of 4.5% on natural images, revealing a new frontier in generative data strategies.

Irregular time series forecasting can be revolutionized with DNBNet, which eliminates bias and adapts to diverse temporal patterns for superior predictive performance.

Relying on MSE for irregular time-series forecasting can lead to misleading evaluations, as it fails to account for timestamp sampling biases.

GLAIM achieves state-of-the-art imputation performance by seamlessly combining stable global and adaptive local inter-variable dependencies, setting a new benchmark for handling multivariate time series with missing data.