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RCC-GAN: Regularized Compound Conditional GAN for Large-Scale Tabular Data Synthesis
We refer to this noble definition as compound conditional vector and employ it for training the generator network.
Bi-Discriminator Class-Conditional Tabular GAN
This paper introduces a bi-discriminator GAN for synthesizing tabular datasets containing continuous, binary, and discrete columns.
On the Effectiveness of Interpretable Feedforward Neural Network
We conclude by finding that the generalized IFFNNs achieve comparable classification performance to their normal feedforward neural network counterparts and provide meaningful interpretations.
Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems
In this paper, we propose a new framework for mitigating biases in machine learning systems.
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