Methods › General › Deep Tabular Learning › VIME
Value Imputation and Mask Estimation
VIME
Introduced by Jinsung Yoon et al. in VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
VIME , or Value Imputation and Mask Estimation, is a self- and semi-supervised learning framework for tabular data. It consists of a pretext task of estimating mask vectors from corrupted tabular data in addition to the reconstruction pretext task for self-supervised learning.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain 1 Dec 2020 · 2 repositories
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Data Augmentation | 1 |
| Imputation | 1 |
| Self-Supervised Learning | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections