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"
TAT-QA (Tabular And Textual dataset for Question Answering) contains 16,552 questions associated with 2,757 hybrid contexts from real-world financial reports.
A comprehensive toolkit and benchmark for tabular data learning, featuring over 20 deep methods, more than 10 classical methods, and 300 diverse tabular datasets.
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"
Load and summarize your data efficiently and with control! Use this platform and expand for your own applications.
Preparatory scripts for BIDS tabular phenotypic data in large neuroimaging datasets.
A powerful annex BUBT, BUBT Soft, and BUBT website scraping script.
Omnipy is a high level Python library for type-driven data wrangling and scalable workflow orchestration (under development)
Official pytorch implementation codes for NeurIPS-2023 accepted paper "Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation"
[ICML 2024] BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model
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