65 papers with code • 0 benchmarks • 1 datasets
These leaderboards are used to track progress in Distributed Computing
We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications.
The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning.
Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently.
By the use of operator-splitting we decouple the simulation of reaction-diffusion kinetics inside the cells from the simulation of molecular cell-cell interactions occurring on the boundaries between cells.
Such a method should scale up well, model the heterogeneous noise, and address the communication issue in a distributed system.
Beyond an educational resource for ML, the browser has vast potential to not only improve the state-of-the-art in ML research, but also, inexpensively and on a massive scale, to bring sophisticated ML learning and prediction to the public at large.