Research on Tabular Deep Learning: Papers & Packages

[ICML 2023] The official implementation of the paper "TabDDPM: Modelling Tabular Data with Diffusion Models"

Reactive datatables for large or small datasets

A comprehensive toolkit and benchmark for tabular data learning, featuring 30 deep methods, more than 10 classical methods, and 300 diverse tabular datasets.

csvreader library / gem - read tabular data in the comma-separated values (csv) format the right way (uses best practices out-of-the-box with zero-configuration)

Useful code for tabular modeling and automation.

Mambular 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.

R interface to fast.ai

TAT-QA (Tabular And Textual dataset for Question Answering) contains 16,552 questions associated with 2,757 hybrid contexts from real-world financial reports.

Connect to PBI/AAS Models via Python

Data annotator for machine learning allows you to centrally create, manage and administer annotation projects for machine learning

A framework for dynamically creating a tabular model based on an Excel template.

OLAP PivotTable Extensions is an Excel add-in which extends the functionality of PivotTables on Analysis Services cubes, the Excel Data Model (Power Pivot), and Power BI

Official PyTorch implementation of STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables (ICLR 2023 Spotlight).

Awesome Comma-Separated Values (CSV) - What's Next? - Frequently Asked Questions (F.A.Q.s) - Libraries & Tools

A high-performance and low-allocating framework to work with tabular data

ML models + benchmark for tabular data classification and regression

Converts XML to LaTeX

Easily get custom go template based outputs to your command-line tool. Like in docker/kubernetes

Repository of web and code editor friendly Observable Data Toools 🛠️ and Notebooks 📚 in .js, .nb.json, .ojs, .omd, .html and .qmd document formats for Data Previews in a browser and in VSCode IDE with Observable JS extension, Quarto extension, and new Quarto publishing tools.