A new corpus of 108 mixed string-numeric tables shows that advanced tabular learners with basic string embeddings perform well on most real-world data, while large LLM encoders help on free-text heavy tables.
arXiv preprint arXiv:2408.06291 (2025)
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A new corpus of 108 mixed string-numeric tables shows that advanced tabular learners with basic string embeddings perform well on most real-world data, while large LLM encoders help on free-text heavy tables.
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From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning
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Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models
Benchmarks TabPFN, MambaNet and MambaAttention on imbalanced EV crash severity classification with SMOTEENN resampling on Texas data, identifying intersection relation and speed limit as top features and MambaAttention as strongest on severe cases.