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cs.LG2026
Hyperparameter Transfer Laws for Non-Recurrent Multi-Path Neural Networks
Shenxi Wu, Haosong Zhang, Xingjian Ma +4
Deeper modern architectures are costly to train, making hyperparameter transfer preferable to expensive repeated tuning. Maximal Update Parametrization (P) helps explain why man…
cs.LG2024
Utilizing Causal Network Markers to Identify Tipping Points ahead of Critical Transition
Shirui Bian, Zezhou Wang, Siyang Leng +2
Early-warning signals of delicate design are always used to predict critical transitions in complex systems, which makes it possible to render the systems far away from the catastr…