Liquid Neural Network-based Adaptive Learning Outperforms Incremental Learning for Link Load Prediction amid Drastic Concept Drift due to Network Failures

Liquid neural networks can adapt to drastic changes in network traffic patterns caused by failures without the need for retraining, outperforming incremental learning approaches in such scenarios.