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Found 3 papers, 3 papers with code
TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks
In this work, we analyze existing tabular benchmarks and find two common characteristics of tabular data in typical industrial applications that are underrepresented in the datasets usually used for evaluation in the literature.
TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023
Deep learning (DL) models for tabular data problems (e. g. classification, regression) are currently receiving increasingly more attention from researchers.
Shifts 2.0: Extending The Dataset of Real Distributional Shifts
This creates a need to be able to assess how robustly ML models generalize as well as the quality of their uncertainty estimates.
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