Sebastian Steindl
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This paper empirically evaluates LLM-generated reviews for academic papers, finding that while LLM reviews show some alignment with human ones, authors can effectively 'game' the system using iterative revision to significantly boost paper scores.
NetVAD proposes a novel, identifier-free Variational Autoencoder that leverages frozen Foundation Models to achieve highly competitive unsupervised performance for zero-day intrusion detection.
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NetVAD: Foundation-Model Representation Learning for Identifier-Free Unsupervised Intrusion Detection
NetVAD proposes a novel, identifier-free Variational Autoencoder that leverages frozen Foundation Models to achieve highly competitive unsupervised performance for zero-day intrusion detection.