435 citations · 465 across the 7 of their papers we have counts for
8 papers
Lion Cub: Minimizing Communication Overhead in Distributed Lion
Satoki Ishikawa, Tal Ben-Nun, Brian Van Essen +2
Communication overhead is a key challenge in distributed deep learning, especially on slower Ethernet interconnects, and given current hardware trends, communication is likely to b…
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets
Sam Ade Jacobs, Brian Van Essen, David Hysom +11
Training deep neural networks on large scientific data is a challenging task that requires enormous compute power, especially if no pre-trained models exist to initialize the proce…
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications
Shusen Liu, Di Wang, Dan Maljovec +13
With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First…
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism
Nikoli Dryden, Naoya Maruyama, Tom Benson +3
Scaling CNN training is necessary to keep up with growing datasets and reduce training time. We also see an emerging need to handle datasets with very large samples, where memory r…
Distinguishing between Normal and Cancer Cells Using Autoencoder Node Saliency
Ya Ju Fan, Jonathan E. Allen, Sam Ade Jacobs +1
Gene expression profiles have been widely used to characterize patterns of cellular responses to diseases. As data becomes available, scalable learning toolkits become essential to…
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches
Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic +6
Deep learning has demonstrated tremendous success in variety of application domains in the past few years. This new field of machine learning has been growing rapidly and applied i…