Hyperparameter Learning for Latent Factorization of Tensors for Representation Learning to Large-scale Dynamic Weighted Directed Network

In three linesAutomated hyperparameter optimization framework using Differential Evolution (DE) for Latent Factorization of Tensors (LFT) on large-scale dynamic weighted directed networks. The method automatically learns regularization parameters λ₁, λ₂, λ₃ during training, eliminating manual tuning. Experiments on 4 real-world datasets show lower MAE and RMSE versus manually tuned baselines.

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