Character distribution patterns differ between humans and AI in domain-specific ways, enabling improved AI text detection via the new LD-Score when combined with existing tools on the MDTA benchmark.
IEEE Transactions on Information theory , volume=
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A self-supervised approach uses consistent spatial relationships of anatomical structures across patients to improve 3D multi-modal medical image representations, yielding modest gains on segmentation and classification tasks.
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Beyond Perplexity: Character Distribution Signatures and the MDTA Benchmark for AI Text Detection
Character distribution patterns differ between humans and AI in domain-specific ways, enabling improved AI text detection via the new LD-Score when combined with existing tools on the MDTA benchmark.
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Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging
A self-supervised approach uses consistent spatial relationships of anatomical structures across patients to improve 3D multi-modal medical image representations, yielding modest gains on segmentation and classification tasks.