Search Results for author:
Found 2 papers, 2 papers with code
MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets
A recent technical breakthrough in the domain of machine learning is the discovery and the multiple applications of Generative Adversarial Networks (GANs).
Distributed deep learning on edge-devices: feasibility via adaptive compression
We report a reduction of the total amount of data sent by workers to the server by two order of magnitude (e. g., 191-fold reduction for a convolutional network on the MNIST dataset), when compared to a standard asynchronous stochastic gradient descent, while preserving model accuracy.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
