⚡ TabPFN: Foundation Model for Tabular Data ⚡

Zero-shot Time Series Forecasting with TabPFN (work accepted at NeurIPS 2024 TRL and TSALM workshops)

Community extensions for TabPFN - the foundation model for tabular data. Built with TabPFN! 🤗

⚡ Easy API access to the tabular foundation model TabPFN ⚡

Semi-automatic feature engineering process using Language Models and your dataset descriptions. Based on the paper "LLMs for Semi-Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering" by Hollmann, Müller, and Hutter (2023).

Code for finetuning TabPFN on one downstream tabular dataset.

TabPFGen: Synthetic Tabular Data Generation with TabPFN

On Finetuning Tabular Foundation Models Paper Code

Ensemble-based, size-agnostic wrapper for the TabPFN classifier

Turning Tabular Foundation Models into Graph Foundation Models

A curated collection of papers, repositories, and resources on Prior-data Fitted Networks (PFNs).

[ICML 2024] TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsetting

TFG realizado en la Universidad de Burgos del desarrollo de una aplicación para el uso de un Radar de 60 GHz de la marca Acconeer.

TabPFN implementation with PyTorch

A comprehensive pipeline for multi-model classification of PET/CT radiomics data, including individual classifiers and decision-level fusion via soft voting. ROC curves are used to evaluate performance on both individual models and the ensemble.

Code for evaluating TabPFN against other classifiers on engineering design problems.

Stabilization of classification ML-models using synthetic data with outliers on Open Data.

PyTorch normalization layer that learns Yeo-Johnson power scaling on tabular data

A community project to build a RESTful API wrapper for tabpfn-client, the tabular foundational model by https://priorlabs.ai/

Efficient autoregressive inference for TabPFN models

My Small PFN — a competitive proof-of concept for Prior-Data Fitted Networks

Benchmarking the performance of TabPFNClassifier against traditional machine learning models (Logistic Regression, SVM, k-NN, Decision Tree, GaussianNB) on tabular datasets. Includes metrics like accuracy, fit time, and predict time, with interactive visualizations.

Foundation model wrapper for thin-data pricing — TabPFN v2/TabICLv2 backend, GLM benchmark, PDP relativities, CommitteeReport

Benchmarking of ML and Deep Learning models (MLP, Random Forest, XGBoost, STab, TabPFN2) for customer churn prediction in the telecom industry.

Transfer learning and foundation models for thin-segment pricing - GLMTransfer, TabPFN wrapper, MMD shift test

ML competition submission to classify anonymous age related condition

Reproducible pipelines for TB classification using TabPFN and traditional machine learning models.