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cs.LG2026
Discretizing Continuous Time Series for Imputation with Masked Diffusion Training
Dongbin Kim, Seungyun Lee, Geonwoo Shin +1
Time series imputation is a crucial area for reliable time series analysis, yet it remains challenging due to the complex temporal dynamics and noise of real-world data. Existing a…
cs.LG2024
BayesNAM: Leveraging Inconsistency for Reliable Explanations
Hoki Kim, Jinseong Park, Yujin Choi +2
Neural additive model (NAM) is a recently proposed explainable artificial intelligence (XAI) method that utilizes neural network-based architectures. Given the advantages of neural…
cs.LG2024
TimeBridge: Better Diffusion Prior Design with Bridge Models for Time Series Generation
Jinseong Park, Seungyun Lee, Woojin Jeong +2
Time series generation is widely used in real-world applications such as simulation, data augmentation, and hypothesis testing. Recently, diffusion models have emerged as the de fa…