AMI reduces sensor usage by 48.8% and improves accuracy by 1.9% on average across three medical datasets by jointly learning when to sense and how to infer from multimodal physiological signals.
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Formalizes proxy tasks and a protocol for CSAI detection model design that avoids direct use of sensitive data, demonstrated via few-shot indoor scene classification with reported success on real CSAI imagery.
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Sense Less, Infer More: Agentic Multimodal Transformers for Edge Medical Intelligence
AMI reduces sensor usage by 48.8% and improves accuracy by 1.9% on average across three medical datasets by jointly learning when to sense and how to infer from multimodal physiological signals.
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Minimizing Risk Through Minimizing Model-Data Interaction: A Protocol For Relying on Proxy Tasks When Designing Child Sexual Abuse Imagery Detection Models
Formalizes proxy tasks and a protocol for CSAI detection model design that avoids direct use of sensitive data, demonstrated via few-shot indoor scene classification with reported success on real CSAI imagery.