Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization

Documenti analoghi

• GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment
  di: Hou, Zhenyu, et al.
  Pubblicazione: (2024)
• Generalization of Geometric Graph Neural Networks with Lipschitz Loss Functions
  di: Wang, Zhiyang, et al.
  Pubblicazione: (2024)
• Accelerating High-Efficiency Organic Photovoltaic Discovery via Pretrained Graph Neural Networks and Generative Reinforcement Learning
  di: Qiu, Jiangjie, et al.
  Pubblicazione: (2025)
• Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending against Poisoning Attacks
  di: Liu, Ao, et al.
  Pubblicazione: (2024)
• Classification with Deep Neural Networks and Logistic Loss
  di: Zhang, Zihan, et al.
  Pubblicazione: (2023)