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cs.LG2024
LAYUP: Asynchronous decentralized gradient descent with LAYer-wise UPdates
Cabrel Teguemne Fokam, Marcel Nieveler, Lukas König +3
The increasing size of deep learning models has made distributed training across multiple devices essential. Synchronous, centralized methods incur large communication and synchron…
stat.ML2024
AR-Sieve Bootstrap for the Random Forest and a simulation-based comparison with rangerts time series prediction
Cabrel Teguemne Fokam, Carsten Jentsch, Michel Lang +1
The Random Forest (RF) algorithm can be applied to a broad spectrum of problems, including time series prediction. However, neither the classical IID (Independent and Identically d…