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tabular-deep-learning

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OpenTabular / DeepTab

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DeepTab is a Python package that simplifies tabular deep learning by providing a suite of models for regression, classification, and distributional regression tasks. It includes models such as Mambular, TabM, FT-Transformer, TabulaRNN, TabTransformer, and tabular ResNets.

node regression classification rnn resnet deeplearning preprocessing ssm mamba tabular saint state-space-models ple distributional-regression tabtransformer ft-transformer piecewise-linear-encoding tabular-learning tabular-deep-learning tabm
  • Updated Mar 3, 2026
  • Python

tk-yasuno / tabgam_additive

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This study proposes a diverse family of additive models that extend Generalized Additive Models to achieve both interpretability and predictive performance in forecasting. While preserving the stability of traditional GAMs, we develop Feature-wise Additive Models, Gradient Boosting Additive Models, and TabNet-based Neural Additive Models (TabNAM).

forecasting ensemble-learning generalized-additive-models interpretable-ml explainable-ai tabnet tabular-deep-learning neural-additive-models tabnam boosting-additive-models feature-wise-additive-models
  • Updated Mar 15, 2026
  • Python

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