Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
在大语言模型、多模态大语言模型及更广泛的领域中进行模型融合:方法、理论、应用与机遇
Enneng Yang, Li Shen, Guibing Guo, Xingwei Wang, Xiaochun Cao, Jie Zhang, Dacheng Tao
机构
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Shenzhen Campus of Sun Yat-sen University, China(中山大学深圳校区)
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Northeastern University China(东北大学)
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Shenzhen Campus of Sun Yat-sen University China(中山大学深圳校区)
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Nanyang Technological University Singapore(南洋理工大学)
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Northeastern University(东北大学)
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Shenzhen Campus of Sun Yat-sen University(中山大学深圳校区)
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Nanyang Technological University(南洋理工大学)
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Institute for Clarity in Documentation Dublin Ohio USA(文档清晰研究所)
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Inria Paris-Rocquencourt Rocquencourt France(巴黎-罗quentourt研究所)
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Rajiv Gandhi University Doimukh Arunachal Pradesh India(拉贾·甘地大学)
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Tsinghua University Haidian Qu Beijing Shi China(清华大学)
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Palmer Research Laboratories San Antonio Texas USA(帕勒研究中心)
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Institute for Clarity in Documentation(文档清晰研究所)
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Inria Paris-Rocquencourt(巴黎-罗quentourt研究所)
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Rajiv Gandhi University(拉贾·甘地大学)
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Tsinghua University(清华大学)
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Palmer Research Laboratories(帕勒研究中心)
TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems
噪声黑盒问题的TESO禁忌增强仿真优化
Bulent Soykan, Sean Mondesire, Ghaith Rabadi
机构
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Institute for Simulation and Training(模拟与培训研究所)
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University of Central Florida(中央佛罗里达大学)
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School of Modeling, Simulation, and Training(建模、模拟与培训学院)
专题命中
其他多模态
:multimodal(abstract);分类 cs.AI
AI总结
TESO通过结合禁忌列表和精英记忆,提升噪声黑盒问题的仿真优化效率与稳定性。
Comments11 pages, 2 figures, Presented at the Winter Simulation Conference 2025, Seattle, Washington (December 2025)