RTDL (Research on Tabular Deep Learning) is a collection of papers and packages on deep learning for tabular data.
🔔 To follow announcements on new papers and projects:
- Follow the authors of the papers on Twitter and GitHub.
- Subscribe to releases in this GitHub repository: "Watch -> Custom -> Releases".
Note
The previous rtdl package is now replaced
with individual packages (see the next sections).
If you used rtdl, please, read the details.
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- This repository is NOT deprecated.
- However, the package
rtdlis deprecated and replaced with individual packages. - If you used the latest
rtdl==0.0.13installed from PyPI (not from GitHub!) aspip install rtdl, then the same models (MLP, ResNet, FT-Transformer) can be found in thertdl_revisiting_modelspackage, though API is slightly different. - ❗ If you used the unfinished code from the main branch, it is highly
recommended to switch to the new packages. In particular,
the unfinished implementation of embeddings for continuous features
contained many unresolved issues (the new
rtdl_num_embeddingspackage, in turn, is more efficient and correct).
The documentation is available through the "Package" links in the "Papers" section.
The following snippet installs all available packages including optional dependencies.
pip install rtdl_num_embeddings
pip install rtdl_revisiting_models
pip install "scikit-learn>=1.0,<2"
(2023) TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023
Paper
Code
(2022) TabDDPM: Modelling Tabular Data with Diffusion Models
Paper
Code
(2022) Revisiting Pretraining Objectives for Tabular Deep Learning
Paper
Code
(2022) On Embeddings for Numerical Features in Tabular Deep Learning
Paper
Code
Package (rtdl_num_embeddings)
(2021) Revisiting Deep Learning Models for Tabular Data
Paper
Code
Package (rtdl_revisiting_models)
(2019) Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data
Paper
Code
