Research on Tabular Deep Learning: Papers & Packages

A comprehensive toolkit and benchmark for tabular data learning, featuring 35+ deep methods, more than 10 classical methods, and 300 diverse tabular datasets.

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

Reactive datatables for large or small datasets

ML models + benchmark for tabular data classification and regression

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.

The MCP Engine is a Power BI tool that lets AI assistants like Claude interact with your Power BI models programmatically: read your model structure, run DAX queries, create and modify measures, manage relationships, and perform advanced analytics - all through natural conversation.

Useful code for tabular modeling and automation.

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)

Implementation of the sap-rpt-1-oss deep learning model with inference pipeline as described in the paper "ConTextTab: A Semantics-Aware Tabular In-Context Learner".

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

R interface to fast.ai

My collection of wallpapers!

Awesome Tabular Deep Learning for "Representation Learning for Tabular Data: A Comprehensive Survey"

Connect to PBI/AAS Models via Python

This is a curated list of research on diffusion models for tabular data, and serves as the official repository for the survey paper "Diffusion Models for Tabular Data: Challenges, Current Progress, and Future Directions"

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

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

Scaling In-context Learning from Few-shot to 1,024-shot on Tabular ML

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

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

Converts XML to LaTeX

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

On Finetuning Tabular Foundation Models Paper Code

augini: AI-Powered Tabular Data Assistant

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.

GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data

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

A tool for managing the processing of Analysis Services, Azure Analysis Services and Power BI Premium models.