Diffusion-Model-Leiden/awesome-diffusion-models-for-tabular-data
This is a curated list of research on diffusion models for tabular data, and serves as the official repository for the survey paper "Diffusion Models for Tabular Data: Challenges, Current Progress, and Future Directions"
This is a curated list of research papers exploring how diffusion models can be used with tabular data. It provides an overview of various techniques for generating new data, filling in missing information, enhancing data privacy, and detecting anomalies in datasets. This resource is for data scientists, machine learning engineers, and researchers interested in applying advanced generative AI to structured data.
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Use this if you need to understand the landscape of diffusion models for tabular data and explore techniques for data augmentation, imputation, privacy-preserving synthesis, or anomaly detection.
Not ideal if you are looking for a ready-to-use software library or a step-by-step tutorial for implementing these models, as this is a research overview.
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