Snapshot Ensemble

Snapshot ensembles are a technique for improving the robustness and performance of predictive models by training and combining multiple models at different stages of training, effectively creating a diverse ensemble at the cost of training a single model. Current research focuses on applying this approach to various domains, including link prediction in knowledge graphs, temporal graph analysis (using both event-based and snapshot-based data), and dynamic graph neural networks, often incorporating techniques like Gaussian splatting or Hawkes processes to enhance model efficiency and scalability. This methodology offers significant advantages in terms of computational efficiency and prediction accuracy across diverse applications, ranging from real-time video streaming to improved classification tasks in various fields.