• Deep learning for privacy preservation in autonomous moving platforms enhanced 5G heterogeneous networks 

  Wu, Y; Ma, Y; Dai, H-N; et al. (Elsevier, 15 December 2020)

  5G heterogeneous networks have become a promising platform to connect a growing number of Internet-of-Things (IoT) devices and accommodate a wide variety of vertical services. IoT has not been limited to traditional sensing ...

• A Graph Neural Network-based Digital Twin for Network Slicing Management 

  Wang, H; Wu, Y; Min, G; et al. (Institute of Electrical and Electronics Engineers (IEEE), 29 December 2020)

  Network slicing has emerged as a promising networking paradigm to provide resources tailored for Industry 4.0 and diverse services in 5G networks. However, the increased network complexity poses a huge challenge in network ...

• Deep learning generalizes because the parameter-function map is biased towards simple functions 

  Pérez, GV; Camargo, CQ; Louis, AA (ICLR, 9 May 2019)

  Deep neural networks (DNNs) generalize remarkably well without explicit regularization even in the strongly over-parametrized regime where classical learning theory would instead predict that they would severely overfit. ...

• Complexity Explained 

  De Domenico, M; Brockmann, D; Camargo, CQ; et al. (Complexity Explained project, 27 June 2019)

  Booklet of the Complexity Explained project

• New Methods for the Steady-State Analysis of Complex Agent-Based Models 

  Camargo, CQ (Frontiers Media, 8 April 2020)

  Among all tools used to understand collective human behavior, few tools have been as successful as agent-based models (ABMs). These models have been particularly effective at describing emergent social behavior, such as ...

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