All Outputs (2)

A Novel Evaluation Metric for Synthetic Data Generation (2020)
Presentation / Conference Contribution
Galloni, A., Lendák, I., & Horváth, T. (2020, November). A Novel Evaluation Metric for Synthetic Data Generation. Presented at IDEAL 2020: 21st International Conference on Intelligent Data Engineering and Automated Learning, Guimarães, Portugal

Differentially private algorithmic synthetic data generation (SDG) solutions take input datasets Dp consisting of sensitive, private data and generate synthetic data Ds with similar qualities. The importance of such solutions is increasing both becau... Read More about A Novel Evaluation Metric for Synthetic Data Generation.

Data Generation Using Gene Expression Generator (2020)
Presentation / Conference Contribution
Farou, Z., Mouhoub, N., & Horváth, T. (2020, November). Data Generation Using Gene Expression Generator. Presented at IDEAL 2020: 21st International Conference on Intelligent Data Engineering and Automated Learning, Guimarães, Portugal

Generative adversarial networks (GANs) could be used efficiently for image and video generation when labeled training data is available in bulk. In general, building a good machine learning model requires a reasonable amount of labeled training data.... Read More about Data Generation Using Gene Expression Generator.