Tab-Ddpm

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tab-ddpm is an open-source Coding & Development tool that provides the official implementation for modeling tabular data with diffusion models. It allows researchers to replicate and extend the TabDDPM model for synthetic data generation.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is tab-ddpm?

tab-ddpm is the official open-source implementation of the paper "TabDDPM: Modelling Tabular Data with Diffusion Models" presented at ICML 2023. This tool provides researchers and developers with the necessary code to train, sample, and evaluate TabDDPM for generating synthetic tabular data. It includes scripts for hyperparameter tuning, evaluation against baselines like SMOTE and CTGAN, and privacy calculation. The repository also offers pre-tuned hyperparameters for evaluation models and provides access to datasets used in the paper, making it a comprehensive resource for experimentation and development in the field of AI and machine learning, particularly for those working with tabular datasets and diffusion models.

Best used for

Ideal for AI researchers and machine learning engineers who need to implement and experiment with diffusion models for tabular data, generate high-quality synthetic datasets, and benchmark model performance. Especially valuable for academic research and development in privacy-preserving data generation.

Common actions

model tabular data

generate synthetic data

implement diffusion models

evaluate AI models

replicate research

open-sourceworkflowscollaboration"AI Agents"face swappinglow-code/no-codedeepfakegithub copilotautomated workflow

Capabilities

Key features

TabDDPM model implementation
Hyperparameter tuning scripts
Synthetic data generation
Evaluation against baselines
Privacy calculation
Pre-tuned evaluation models

Target Audience

AI researcherMachine learning engineerprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of data can TabDDPM model?

TabDDPM is specifically designed for modeling tabular data. This includes datasets commonly found in various domains, often structured in rows and columns, which can contain a mix of numerical and categorical features.

What are the system requirements to run tab-ddpm?

The tool requires Python 3.9.7 and specific versions of PyTorch (1.10.1+cu111). It is recommended to use a Conda environment for setup. While not explicitly stated as a requirement, the example run time mentions an NVIDIA GeForce RTX 2080 Ti, suggesting GPU acceleration is beneficial.

Can I use my own datasets with tab-ddpm?

Yes, while the repository provides datasets used in the paper, the framework is designed to be adaptable. You can load your own tabular datasets to train, sample, and evaluate the TabDDPM model, following the provided file structure and configuration guidelines.

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Code Assistants DevOps & Infrastructure No-Code / Low-Code Testing & QA Backend & APIs Prompt Engineering

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Also listed in

This tool also appears in

Research & Education › Academic Research Data & Analytics › Statistical & Scientific

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