Hyperparameter Learning for Latent Factorization of Tensors for Representation Learning to Large-scale Dynamic Weighted Directed Network
arXiv:2606.09880v1 Announce Type: new Abstract: Large-scale dynamic weighted directed networks (DWDNs) are widely used to model time-varying interactions among nodes. Latent factorization of tensors (LFT) extracts target knowledge from DWDNs via low-rank embedding. However, similar to many machine learning models, the performance of LFT heavily depends on the selection of hyperparameters. In practice, these parameters are often tuned manually or through grid search, which requires significant computational resources and human effort. Motivated by this challenge, this paper proposes an automate
The increasing complexity and scale of deep learning models necessitate more efficient and automated hyperparameter optimization methods to reduce computational costs and human effort.
Automating hyperparameter tuning for advanced tensor factorization techniques will improve the efficiency and applicability of AI models for complex dynamic networks, potentially accelerating research and development in various fields.
The reliance on manual or grid search hyperparameter tuning for Latent Factorization of Tensors (LFT) will diminish, leading to faster model deployment and better performance in large-scale dynamic networks.
- · AI researchers
- · Data scientists
- · Companies using complex network analysis
- · Cloud computing providers
- · Manual hyperparameter tuning experts
More sophisticated AI models become accessible and easier to deploy due to automated tuning.
This could lead to breakthroughs in areas that rely on analyzing large dynamic networks, such as anomaly detection in cybersecurity or predictive analytics for infrastructure.
Reduced barriers to entry for developing complex AI systems might democratize advanced AI capabilities, fostering broader innovation across industries.
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Read at arXiv cs.LG