Software for evaluating the quality of synthetic data compared with real data.
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Updated
May 31, 2024 - Python
Software for evaluating the quality of synthetic data compared with real data.
A terminal spreadsheet multitool for discovering and arranging data
Benchmarking synthetic data generation methods.
A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
A standard framework for modelling Deep Learning Models for tabular data
This repository automates the creation of output tabulations for publication in .xlsx files from a Pandas DataFrame
Fast and Accurate ML in 3 Lines of Code
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
A novel approach for synthesizing tabular data using pretrained large language models
We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.
A library to model multivariate data using copulas.
Infer SQL DDL statements from tabular data.
Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data (Nature Communications, 2023)
RuTaBERT is a model solving the problem of Column Type Annotation with pre-trained large language model (BERT), trained on the Russian corpus.
A very basic implementation of a preprocessor for tabular data.
Quick, easy and pretty display of tabular data or matrices, with optional ANSI color and borders
What's in your data? Extract schema, statistics and entities from datasets
A PyTorch Lightning-based library for self- and semi-supervised learning on tabular data.
Algorithms for outlier, adversarial and drift detection
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
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