2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.DC2019
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…
cs.LG2019★ 2 cited
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…
cs.LG2019★ 1 cited
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…