AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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ECHO-$k$ is introduced, a task-agnostic and self-supervised learning principle for modality acquisition that uses a deep model's internal pretrained representations as proxy targets that summarize cross-modal information and consistently improves budgeted downstream performance across diverse foundation-model backends.
VirTues-derived biomarkers predict anti-PD-L1 chemo-immunotherapy response and stratify disease-free survival in an independent cohort, outperforming state-of-the-art biomarkers derived from the same datasets and current clinical stratification schemes.
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