5 citations · 7 across the 2 of their papers we have counts for
3 papers
cs.LG2024
TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data
Namjoon Suh, Yuning Yang, Din-Yin Hsieh +4
We present TimeAutoDiff, a unified latent-diffusion framework for four fundamental time-series tasks: unconditional generation, missing-data imputation, forecasting, and time-varyi…
cs.LG2024★ 2 cited
Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective
Din-Yin Hsieh, Chi-Hua Wang, Guang Cheng
Exploring generative model training for synthetic tabular data, specifically in sequential contexts such as credit card transaction data, presents significant challenges. This pape…
stat.ML2023★ 5 cited
AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing
Namjoon Suh, Xiaofeng Lin, Din-Yin Hsieh +2
Diffusion model has become a main paradigm for synthetic data generation in many subfields of modern machine learning, including computer vision, language model, or speech synthesi…