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Showing 1–4 of 4 results
Advanced filters: Author: Faqiang Liu Clear advanced filters

  • Energy-efficient, task-agnostic continual learning is a key challenge in Artificial Intelligence frameworks. Here, authors propose a hybrid neural network that emulates dual representations in corticohippocampal circuits, reducing the effect of catastrophic forgetting.

    • Qianqian Shi
    • Faqiang Liu
    • Rong Zhao
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-14

  • Hybrid neural networks combine advantages of spiking and artificial neural networks in the context of computing and biological motivation. The authors propose a design framework with hybrid units for improved flexibility and efficiency of hybrid neural networks, and modulation of hybrid information flows.

    • Rong Zhao
    • Zheyu Yang
    • Luping Shi
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-12

  • This study designs a new model based on medium-resolution satellite imagery to assess building damage from war, using the cases of Syria and Ukraine. It found that building damage has broader consequences for the population affected, especially when accounting for hospitals and schools.

    • Zhengyang Hou
    • Ying Qu
    • Chenghu Zhou
    Research
    Nature Cities
    Volume: 1, P: 359-369

  • Shortcut learning, where models exploit spurious correlations in data, leads to brittle predictions and undermines fair assessment of model performance. Here, the authors introduce a diagnostic paradigm—shortcut hull learning—that identifies shortcuts from datasets, enabling unbiased evaluation of model capabilities.

    • Wenhao Zhou
    • Faqiang Liu
    • Rong Zhao
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-15