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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Research on Tabular Deep Learning: Papers & Packages
| Date | Stars |
|---|---|
| 2026-07-24 | 1153 |
| 2026-07-25 | 1154 |
| 2026-07-28 | 1154 |
| 2026-07-30 | 1154 |
| 2026-07-31 | 1154 |
| 2026-08-06 | 1154 |
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# RTDL (Research on Tabular Deep Learning) RTDL (**R**esearch on **T**abular **D**eep **L**earning) is a collection of papers and packages on deep learning for tabular data. :bell: *To follow announcements on new projects, subscribe to releases in this GitHub repository: "Watch -> Custom -> Releases".* > [!NOTE] > The list of projects below is up-to-date, but the `rtdl` Python package is deprecated. > If you used the <code>rtdl</code> package, please, read the details. > > <details> > > 1. First, to clarify, this repository is **NOT** deprecated, > only the package `rtdl` is deprecated: it is replaced with other packages. > 2. If you used the latest `rtdl==0.0.13` installed from PyPI (not from GitHub!) > as `pip install rtdl`, then the same models > (MLP, ResNet, FT-Transformer) can be found in the `rtdl_revisiting_models` package, > though API is slightly different. > 3. :exclamation: **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 `rtdl_num_embeddings` package, in turn, > is more efficient and correct). > > </details> # Papers (2026) Benchmarking Optimizers for MLPs in Tabular Deep Learning <br> [Paper](https://arxiv.org/abs/2604.15297) [Code](https://github.com/yandex-research/tabular-dl-optimizers) (2025) Unveiling the Role of Data Uncertainty in Tabular Deep Learning <br> [Paper](https://arxiv.org/abs/2509.04430) (2025) On Finetuning Tabular Foundation Models <br> [Paper](https://arxiv.org/abs/2506.08982) [Code](https://github.com/yandex-research/tabpfn-finetuning) (2024) TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling <br> [Paper](https://arxiv.org/abs/2410.24210) [Code](https://github.com/yandex-research/tabm) [Usage](https://github.com/yandex-research/tabm#using-tabm-in-practice) (2024) TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks <br> [Paper](https://arxiv.org/abs/2406.19380) [Code](https://github.com/yandex-research/tabred) (2023) TabR: Tabular Deep Learning Meets Nearest Neighbors <br> [Paper](https://arxiv.org/abs/2307.14338) [Code](https://github.com/yandex-research/tabular-dl-tabr) (2022) TabDDPM: Modelling Tabular Data with Diffusion Models <br> [Paper](https://arxiv.org/abs/2209.15421) [Code](https://github.com/yandex-research/tab-ddpm) (2022) Revisiting Pretraining Objectives for Tabular Deep Learning <br> [Paper](https://arxiv.org/abs/2207.03208) [Code](https://github.com/puhsu/tabular-dl-pretrain-objectives) (2022) On Embeddings for Numerical Features in Tabular Deep Learning <br> [Paper](https://arxiv.org/abs/2203.05556) [Code](https://github.com/yandex-research/rtdl-num-embeddings) [Package (rtdl_num_embeddings)](https://github.com/yandex-research/rtdl-num-embeddings/tree/main/package/README.md) (2021) Revisiting Deep Learning Models for Tabular Data <br> [Paper](https://arxiv.org/abs/2106.11959) [Code](https://github.com/yandex-research/rtdl-revisiting-models) [Package (rtdl_revisiting_models)](https://github.com/yandex-research/rtdl-revisiting-models/tree/main/package/README.md) (2019) Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data <br> [Paper](https://arxiv.org/abs/1909.06312) [Code](https://github.com/Qwicen/node)
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:82ec762b616aa11a, topic:deep-learning, topic:neural-network, topic:pytorch