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Found 6 papers, 4 papers with code
An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Models
Tabular data typically contains private and important information; thus, precautions must be taken before they are shared with others.
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations
The problem of processing very long time-series data (e. g., a length of more than 10, 000) is a long-standing research problem in machine learning.
EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting
Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks.
Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular Data Synthesis
First, we can further improve the synthesis quality, by decreasing the negative log-density of real records in the process of adversarial training.
Invertible Tabular GANs: Killing Two Birds with One Stone for Tabular Data Synthesis
First, we can further improve the synthesis quality, by decreasing the negative log-density of real records in the process of adversarial training.
OCT-GAN: Neural ODE-based Conditional Tabular GANs
Synthesizing tabular data is attracting much attention these days for various purposes.
