AutoGraphAD applies a heterogeneous variational graph autoencoder with unsupervised and contrastive learning to detect network anomalies on connection-IP graphs without labeled data, achieving comparable performance to Anomal-E with over an order of magnitude faster training and inference.
Bellman, Dynamic programming,Science.153(3731), 34–37 (1966)
2 Pith papers cite this work. Polarity classification is still indexing.
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Causal PDE-Control Models combine causal drivers with PDE control and filtering to deliver interpretable dynamic portfolio rules that outperform benchmarks in Sharpe ratio and turnover on U.S. equity data.
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AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders
AutoGraphAD applies a heterogeneous variational graph autoencoder with unsupervised and contrastive learning to detect network anomalies on connection-IP graphs without labeled data, achieving comparable performance to Anomal-E with over an order of magnitude faster training and inference.
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Causal PDE-Control Models for Dynamic Portfolio Optimization with Latent Drivers
Causal PDE-Control Models combine causal drivers with PDE control and filtering to deliver interpretable dynamic portfolio rules that outperform benchmarks in Sharpe ratio and turnover on U.S. equity data.