Tag: Robustness
All the articles with the tag "Robustness".
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Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks
This paper proposes a Dynamic Bayesian Network framework for autonomous vehicles that enhances safety in cut-in maneuvers by integrating lateral evidence and probabilistic safety assessments, achieving superior crash avoidance in high-speed scenarios (9.22% crash rate) compared to baseline models in the JRC-FSM simulator.
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MergeBench: A Benchmark for Merging Domain-Specialized LLMs
本文提出MergeBench,一个针对领域专精大型语言模型合并的全面基准测试框架,基于Llama和Gemma模型(2B-9B)评估八种合并方法,揭示了合并在大模型上的优越性、稀疏化和系数调整对知识保留的重要性,并提供了算法选择的实用指南。
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MINGLE: Mixtures of Null-Space Gated Low-Rank Experts for Test-Time Continual Model Merging
MINGLE提出了一种测试时持续模型合并方法,通过混合低秩专家架构和自适应空空间约束门控,利用少量无标签测试样本动态融合模型,显著提升了持续学习中的泛化性能并减少了灾难性遗忘。
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Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets
This paper systematically evaluates machine learning classifiers and imbalance learning techniques on two cybersecurity datasets, revealing that XGB and RF perform robustly, while sampling and ensembling effects vary, emphasizing the need for dataset-specific method selection.
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MELON: Provable Indirect Prompt Injection Defense via Masked Re-execution and Tool Comparison
MELON introduces a novel training-free defense against indirect prompt injection attacks on LLM agents by detecting independence of tool calls from user inputs through masked re-execution, achieving superior attack prevention (0.24% ASR on GPT-4o) and utility preservation (58.78% UA on GPT-4o) compared to existing methods.