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of 3
pro vyhledávání: '"Fokam, Cabrel Teguemne"'
Autor:
Fokam, Cabrel Teguemne, Nazeer, Khaleelulla Khan, König, Lukas, Kappel, David, Subramoney, Anand
The increasing size of deep learning models has created the need for more efficient alternatives to the standard error backpropagation algorithm, that make better use of asynchronous, parallel and distributed computing. One major shortcoming of backp
Externí odkaz:
http://arxiv.org/abs/2410.05985
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 distributed) bootstrap nor block bootstrapping strategies (as implement
Externí odkaz:
http://arxiv.org/abs/2410.00942
Autor:
Kappel, David, Nazeer, Khaleelulla Khan, Fokam, Cabrel Teguemne, Mayr, Christian, Subramoney, Anand
The ubiquitous backpropagation algorithm requires sequential updates through the network introducing a locking problem. In addition, back-propagation relies on the transpose of forward weight matrices to compute updates, introducing a weight transpor
Externí odkaz:
http://arxiv.org/abs/2305.14974