May 16, 2024, 4:42 a.m. | Yan Shen, Fan Yang, Mingchen Gao, Wen Dong

cs.LG updates on arXiv.org arxiv.org

arXiv:2205.02332v3 Announce Type: replace
Abstract: The abundance of data affords researchers to pursue more powerful computational tools to learn the dynamics of complex system, such as neural networks, engineered systems and social networks. Traditional machine learning approaches capture complex system dynamics either with dynamic Bayesian networks and state space models, which is hard to scale because it is non-trivial to prescribe the dynamics with a sparse graph or a system of differential equations; or a deep neural networks, where the …

abstract arxiv bayesian computational cs.lg data dynamic dynamics event interactions learn machine machine learning networks neural networks population replace researchers simulation social social networks space state state space models systems tools traditional machine learning type

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