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input filtering

Input filtering aims to filter out input data that is not necessary for executing model inference, thus reducing data transmission and computing overhead.

Papers

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Showing 1–10 of 13 papers

TitleStatusHype
A Statistical Evaluation of Indoor LoRaWAN Environment-Aware Propagation for 6G: MLR, ANOVA, and Residual Distribution AnalysisCode0
Concept Enhancement Engineering: A Lightweight and Efficient Robust Defense Against Jailbreak Attacks in Embodied AI—0
PiCo: Jailbreaking Multimodal Large Language Models via Pictorial Code Contextualization—0
FLAME: Flexible LLM-Assisted Moderation Engine—0
Damage detection in an uncertain nonlinear beam based on stochastic Volterra series—0
Streamline tractography of the fetal brain in utero with machine learningCode0
Signed-Prompt: A New Approach to Prevent Prompt Injection Attacks Against LLM-Integrated Applications—0
A novel efficient Multi-view traffic-related object detection framework—0
InFi: End-to-End Learning to Filter Input for Resource-Efficiency in Mobile-Centric InferenceCode1
Towards Effective and Robust Neural Trojan Defenses via Input Filtering—0
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