Research on Tabular Deep Learning: Papers & Packages
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Updated
Jul 23, 2024 - Python
Research on Tabular Deep Learning: Papers & Packages
[ICML 2023] The official implementation of the paper "TabDDPM: Modelling Tabular Data with Diffusion Models"
A comprehensive toolkit and benchmark for tabular data learning, featuring over 20 deep methods, more than 10 classical methods, and 300 diverse tabular datasets.
TAT-QA (Tabular And Textual dataset for Question Answering) contains 16,552 questions associated with 2,757 hybrid contexts from real-world financial reports.
Official PyTorch implementation of STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables (ICLR 2023 Spotlight).
Code accompanying AWS blog post "Build a Semantic Search Engine for Tabular Columns with Transformers and Amazon OpenSearch Service"
Multi-modal data augmentation for machine learning
Omnipy is a high level Python library for type-driven data wrangling and scalable workflow orchestration (under development)
Preparatory scripts for BIDS tabular phenotypic data in large neuroimaging datasets.
[ICML 2024] BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model
PyTorch Lightning implementation of PTaRL: Prototype-based Tabular Representation Learning via Space Calibration
Official Implementation of TMLR's paper: "TabCBM: Concept-based Interpretable Neural Networks for Tabular Data"
A powerful annex BUBT, BUBT Soft, and BUBT website scraping script.
Official pytorch implementation codes for NeurIPS-2023 accepted paper "Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation"
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