Semi-supervised Bayesian GANs with log-signatures for uncertainty-aware credit card fraud detection show consistent improvements over benchmarks on the BankSim simulator under varying label proportions.
Using generative adversarial networks for improving classification effectiveness in credit card fraud detection.Information Sciences, 479:448–455
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CGANs with LSTM generator can produce synthetic crypto price series that reproduce temporal patterns and preserve market trends and dynamics.
citing papers explorer
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Semi-Supervised Bayesian GANs with Log-Signatures for Uncertainty-Aware Credit Card Fraud Detection
Semi-supervised Bayesian GANs with log-signatures for uncertainty-aware credit card fraud detection show consistent improvements over benchmarks on the BankSim simulator under varying label proportions.
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Synthetic data in cryptocurrencies using generative models
CGANs with LSTM generator can produce synthetic crypto price series that reproduce temporal patterns and preserve market trends and dynamics.