Baukebrenninkmeijer / On-the-Generation-and-Evaluation-of-Synthetic-Tabular-Data-using-GANsLinks
Repository for the results of my master thesis, about the generation and evaluation of synthetic data using GANs
☆45Updated 2 years ago
Alternatives and similar repositories for On-the-Generation-and-Evaluation-of-Synthetic-Tabular-Data-using-GANs
Users that are interested in On-the-Generation-and-Evaluation-of-Synthetic-Tabular-Data-using-GANs are comparing it to the libraries listed below
Sorting:
- Evaluate real and synthetic datasets against each other☆92Updated 3 weeks ago
- Generating Tabular Synthetic Data using State of the Art GAN architecture☆80Updated 5 years ago
- tableGAN is a synthetic data generation technique (Data Synthesis based on Generative Adversarial Networks paper) based on Generative Ad…☆149Updated 6 years ago
- COR-GAN: Correlation-Capturing Convolutional Neural Networks for Generating Synthetic Healthcare Records☆57Updated 4 years ago
- Generative adversarial training for generating synthetic tabular data.☆292Updated 2 years ago
- Benchmarking synthetic data generation methods.☆277Updated this week
- Synthetic data generation by a Variational AutoEncoder with Differential Privacy assessed using Synthetic Data Vault metrics☆45Updated 2 years ago
- nbsynthetic is simple and robust tabular synthetic data generation library for small and medium size datasets☆68Updated 2 years ago
- Time-series Generative Adversarial Networks (fork from the ML-AIM research group on bitbucket))☆123Updated 3 years ago
- Algorithms for generating synthetic data☆16Updated last year
- TimeSHAP explains Recurrent Neural Network predictions.☆183Updated last year
- A library of Reversible Data Transforms☆127Updated last week
- A Python package for unwrapping ReLU DNNs☆70Updated last year
- Synthetic Data Generation for mixed-type, multivariate time series.☆116Updated last week
- ☆28Updated last year
- Dual Adversarial Autoencoder for Generating Set-valued Sequences☆18Updated 4 years ago
- Metrics to evaluate quality and efficacy of synthetic datasets.☆246Updated this week
- GANs for Time series analysis (Synthetic data generation, anomaly detection and interpolation), Hypertuning using Optuna, MLFlow and Data…☆70Updated 2 years ago
- ☆22Updated last year
- ☆108Updated 2 years ago
- Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021.☆75Updated 9 months ago
- A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and …☆63Updated 3 years ago
- Privacy preserving synthetic data generation workflows☆20Updated 3 years ago
- GAM (Global Attribution Mapping) explains the landscape of neural network predictions across subpopulations☆34Updated last month
- All about explainable AI, algorithmic fairness and more☆110Updated last year
- A lightweight implementation of removal-based explanations for ML models.☆58Updated 4 years ago
- stand alone Neural Additive Models, forked from google-reasearch for easy import to colab☆28Updated 4 years ago
- ☆31Updated 2 years ago
- Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification☆96Updated last year
- CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system☆77Updated 2 years ago