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Revisiting Attention Weights as Explanations from an Information Theoretic Perspective
Attention mechanisms have recently demonstrated impressive performance on a range of NLP tasks, and attention scores are often used as a proxy for model explainability.
Causal-TGAN: Causally-Aware Synthetic Tabular Data Generative Adversarial Network
The flexibility of this architecture is its capability to support different types of expert knowledge (e. g., complete or partial) about the causal nature of the underlying phenomenon.
Causal-TGAN: Generating Tabular Data Using Causal Generative Adversarial Networks
Though there are prior works that have demonstrated great progress, most of them learn the correlations in the data distributions rather than the true processes in which the datasets are naturally generated.
ROMark: A Robust Watermarking System Using Adversarial Training
It protects the copyright of digital content by embedding imperceptible information into the data in the presence of an adversary.
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