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Liquid Neural Network-based Adaptive Learning vs. Incremental Learning for Link Load Prediction amid Concept Drift due to Network Failures
In this work, we address this challenge for the problem of traffic forecasting and propose an approach that exploits adaptive learning algorithms, namely, liquid neural networks, which are capable of self-adaptation to abrupt changes in data patterns without requiring any retraining.
Incremental Recursive Ranking Grouping for Large Scale Global Optimization
However, if a given problem consists of non-additively separable subproblems, DG-based strategies may discover many non-existing interactions.
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