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Open Source Science

https://github.com/yandex-research/rtdl

Research on Tabular Deep Learning: Papers & Packages

https://github.com/yandex-research/rtdl

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
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    DOI references
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    Academic publication links
    Links to: arxiv.org
  • ○
    Committers with academic emails
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (10.8%) to scientific vocabulary

Keywords

ai artificial-intelligence deep-learning machine-learning neural-network papers python pytorch research tabular tabular-data
Last synced: 6 months ago · JSON representation

Repository

Research on Tabular Deep Learning: Papers & Packages

Basic Info
  • Host: GitHub
  • Owner: yandex-research
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 24.6 MB
Statistics
  • Stars: 1,031
  • Watchers: 40
  • Forks: 112
  • Open Issues: 0
  • Releases: 12
Topics
ai artificial-intelligence deep-learning machine-learning neural-network papers python pytorch research tabular tabular-data
Created over 4 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

RTDL (Research on Tabular Deep Learning)

RTDL (Research on Tabular Deep Learning) 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 rtdl package, please, read the 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).

Papers

(2024) TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling
Paper   Code   Usage

(2024) TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks
Paper   Code

(2023) TabR: Tabular Deep Learning Meets Nearest Neighbors
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 (rtdlnumembeddings)

(2021) Revisiting Deep Learning Models for Tabular Data
Paper   Code   Package (rtdlrevisitingmodels)

(2019) Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data
Paper   Code

Owner

  • Name: Yandex Research
  • Login: yandex-research
  • Kind: organization

GitHub Events

Total
  • Release event: 1
  • Watch event: 139
  • Push event: 5
  • Fork event: 14
  • Create event: 1
Last Year
  • Release event: 1
  • Watch event: 139
  • Push event: 5
  • Fork event: 14
  • Create event: 1

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 229
  • Total Committers: 2
  • Avg Commits per committer: 114.5
  • Development Distribution Score (DDS): 0.004
Past Year
  • Commits: 18
  • Committers: 2
  • Avg Commits per committer: 9.0
  • Development Distribution Score (DDS): 0.056
Top Committers
Name Email Commits
Yury Gorishniy s****g@g****m 228
Dendiiiii w****k@1****m 1
Committer Domains (Top 20 + Academic)
126.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 4
  • Total pull requests: 1
  • Average time to close issues: 8 months
  • Average time to close pull requests: 6 months
  • Total issue authors: 4
  • Total pull request authors: 1
  • Average comments per issue: 3.25
  • Average comments per pull request: 5.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 0
  • Average time to close issues: 2 months
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 0
  • Average comments per issue: 5.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • Cgetier520990 (1)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels

Dependencies

pyproject.toml pypi
  • torch >=1.8,<3
environment.yaml conda
  • black 23.10.1.*
  • flit 3.9.0.*
  • isort 5.12.0.*
  • jupyterlab <5
  • mypy 1.6.1.*
  • numpy 1.19.5.*
  • pdoc 14.1.0.*
  • pip <24
  • pytest 7.4.2.*
  • python 3.8.*
  • pytorch 1.8.0.*
  • ruff 0.1.7.*
  • scikit-learn 1.0.2.*
  • tomli 2.0.1.*
  • tqdm 4.66.1.*
  • xdoctest 1.1.2.*