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.
Mambular: A sequential model for tabular deep learning
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DynaTab dynamically reorders features in tabular data via neural rewiring and reports statistically significant gains over 45 baselines on 36 high-dimensional datasets.
Spline encodings for numerical features show task-dependent performance in tabular deep learning, with piecewise-linear encoding robust for classification and variable results for regression depending on spline family, knot strategy, and backbone.
MLP and Attention U-Net outperform other models in reconstructing GRB light curves on 521 events, cutting plateau parameter uncertainties by 37-41% versus the Willingale baseline while achieving low MSE.
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.
citing papers explorer
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STRABLE: Benchmarking Tabular Machine Learning with Strings
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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DynaTab: Dynamic Feature Ordering as Neural Rewiring for High-Dimensional Tabular Data
DynaTab dynamically reorders features in tabular data via neural rewiring and reports statistically significant gains over 45 baselines on 36 high-dimensional datasets.
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From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning
Spline encodings for numerical features show task-dependent performance in tabular deep learning, with piecewise-linear encoding robust for classification and variable results for regression depending on spline family, knot strategy, and backbone.
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Gamma-Ray Burst Light Curve Reconstruction: A Comparative Machine and Deep Learning Analysis
MLP and Attention U-Net outperform other models in reconstructing GRB light curves on 521 events, cutting plateau parameter uncertainties by 37-41% versus the Willingale baseline while achieving low MSE.
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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.