Patient-aware contrastive objective preserves per-patient SR structure in RR-interval embeddings, reaching 0.989 AUROC on patient-independent PAF detection with lower variance than SupCon or BCE baselines.
and Healy, Graham and Smeaton, Alan F
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A post-hoc detection framework exploits generation-induced patterns in autoregressive image outputs to enable provenance tracing across multiple IAR models without altering the generation process.
Empirical benchmarks show distribution similarity between adaptation and pretraining data increases practical privacy leakage in DP-adapted LLMs at fixed theoretical guarantees, with LoRA providing strongest protection for OOD cases.
SSNG replaces sampling-based updates in MHNG with symmetric self-supervised representation alignment using Gumbel-Softmax for discrete messages, yielding higher linear-probe classification accuracy on CIFAR-10 and ImageNet-100 than referential, reconstruction, or MHNG baselines.
citing papers explorer
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Patient-Aware Contrastive Learning Preserves Per-Patient Structure in RR-Interval Representations
Patient-aware contrastive objective preserves per-patient SR structure in RR-interval embeddings, reaching 0.989 AUROC on patient-independent PAF detection with lower variance than SupCon or BCE baselines.
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Data Provenance for Image Auto-Regressive Generation
A post-hoc detection framework exploits generation-induced patterns in autoregressive image outputs to enable provenance tracing across multiple IAR models without altering the generation process.
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Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models
Empirical benchmarks show distribution similarity between adaptation and pretraining data increases practical privacy leakage in DP-adapted LLMs at fixed theoretical guarantees, with LoRA providing strongest protection for OOD cases.
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