Papers input filtering
“input filtering” 태그가 달린 논문 13편 · 필터 해제
A Statistical Evaluation of Indoor LoRaWAN Environment-Aware Propagation for 6G: MLR, ANOVA, and Residual Distribution Analysis
Modeling path loss in indoor LoRaWAN technology deployments is inherently challenging due to structural obstructions, occupant density and activities, and fluctuating environmental conditions. This study proposes a two-s…
Hybrid Machine LearningIndoor Localizationinput filteringInterpretable Machine Learning+1Concept Enhancement Engineering: A Lightweight and Efficient Robust Defense Against Jailbreak Attacks in Embodied AI
Embodied Intelligence (EI) systems integrated with large language models (LLMs) face significant security risks, particularly from jailbreak attacks that manipulate models into generating harmful outputs or executing uns…
input filteringPiCo: Jailbreaking Multimodal Large Language Models via $\textbf{Pi}$ctorial $\textbf{Co}$de Contextualization
Multimodal Large Language Models (MLLMs), which integrate vision and other modalities into Large Language Models (LLMs), significantly enhance AI capabilities but also introduce new security vulnerabilities. By exploitin…
input filteringPICOFLAME: Flexible LLM-Assisted Moderation Engine
The rapid advancement of Large Language Models (LLMs) has introduced significant challenges in moderating user-model interactions. While LLMs demonstrate remarkable capabilities, they remain vulnerable to adversarial att…
Computational Efficiencyinput filteringDamage detection in an uncertain nonlinear beam based on stochastic Volterra series
The damage detection problem in mechanical systems, using vibration measurements, is commonly called Structural Health Monitoring (SHM). Many tools are able to detect damages by changes in the vibration pattern, mainly, …
input filteringStructural Health MonitoringStreamline tractography of the fetal brain in utero with machine learning
Diffusion-weighted magnetic resonance imaging (dMRI) is the only non-invasive tool for studying white matter tracts and structural connectivity of the brain. These assessments rely heavily on tractography techniques, whi…
input filteringSigned-Prompt: A New Approach to Prevent Prompt Injection Attacks Against LLM-Integrated Applications
The critical challenge of prompt injection attacks in Large Language Models (LLMs) integrated applications, a growing concern in the Artificial Intelligence (AI) field. Such attacks, which manipulate LLMs through natural…
input filteringPrompt EngineeringA novel efficient Multi-view traffic-related object detection framework
With the rapid development of intelligent transportation system applications, a tremendous amount of multi-view video data has emerged to enhance vehicle perception. However, performing video analytics efficiently by exp…
input filteringModel Selectionobject-detectionObject DetectionInFi: End-to-End Learning to Filter Input for Resource-Efficiency in Mobile-Centric Inference
Mobile-centric AI applications have high requirements for resource-efficiency of model inference. Input filtering is a promising approach to eliminate the redundancy so as to reduce the cost of inference. Previous effort…
input filteringTowards Effective and Robust Neural Trojan Defenses via Input Filtering
Trojan attacks on deep neural networks are both dangerous and surreptitious. Over the past few years, Trojan attacks have advanced from using only a single input-agnostic trigger and targeting only one class to using mul…
Data Compressioninput filteringVariational InferenceStochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs
Quantized neural networks (NN) are the common standard to efficiently deploy deep learning models on tiny hardware platforms. However, we notice that quantized NNs are as vulnerable to adversarial attacks as the full-pre…
Adversarial DefenseDeep Learninginput filteringProbabilistic Deep LearningReal-time Classification from Short Event-Camera Streams using Input-filtering Neural ODEs
Event-based cameras are novel, efficient sensors inspired by the human vision system, generating an asynchronous, pixel-wise stream of data. Learning from such data is generally performed through event integration into i…
input filteringReal-time Classification from Short Event-Camera Streams using Input-filtering Neural ODEs
Event-based cameras are novel, efficient sensors inspired by the human vision system, generating an asynchronous, pixel-wise stream of data. Learning from such data is generally performed through heavy preprocessing and …
General Classificationinput filteringTime Series Analysis