2 citations · 2 across the 4 of their papers we have counts for
3 papers · 1 filter
Breaking Chains with Trees: Model-Parallel Deep Learning with Time Complexity
Neeraj Mohan Sushma, Aditya Nagarsekar, Cabrel Teguemne Fokam +4
Modern deep neural networks are trained using error backpropagation, which requires sequential forward and backward computations across network layers. As these networks become dee…
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…
Block-local learning with probabilistic latent representations
David Kappel, Khaleelulla Khan Nazeer, Cabrel Teguemne Fokam +2
The ubiquitous backpropagation algorithm requires sequential updates through the network introducing a locking problem. In addition, back-propagation relies on the transpose of for…