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We are deeply committed to advancing causal AI methodologies with the goal of driving transformative change for a brighter future. Our primary focus revolves around understanding and causally influencing the underlying distributions and processes. This approach stands in stark contrast to the mainstream machine learning, including deep learning, which predominantly engages in future prediction and recommends adaptive decisions and actions within existing environments, rather than actively and causally reshaping them.
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论文共 167 篇作者统计合作学者相似作者
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AAAI 2024no. 5 (2024): 4269-4277
Jinan Zou, Qingying Zhao, Yang Jiao,Haiyao Cao, Yanxi Liu,Qingsen Yan,Ehsan Abbasnejad,Lingqiao Liu,Javen Qinfeng Shi
arxiv(2023)
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Qingsen Yan,Mario Fruzangohar,Julian Taylor,Dong Gong, James Walter,Adam Norman,Javen Qinfeng Shi, Tristan Coram
Plant methodsno. 1 (2023): 1-14
arxiv(2023)
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