42 citations · 58 across the 6 of their papers we have counts for
7 papers
Insight into cloud processes from unsupervised classification with a rotationally invariant autoencoder
Takuya Kurihana, James Franke, Ian Foster +2
Clouds play a critical role in the Earth's energy budget and their potential changes are one of the largest uncertainties in future climate projections. However, the use of satelli…
Cloud Classification with Unsupervised Deep Learning
Takuya Kurihana, Ian Foster, Rebecca Willett +6
We present a framework for cloud characterization that leverages modern unsupervised deep learning technologies. While previous neural network-based cloud classification models hav…
3D Convolutional Neural Networks for Dendrite Segmentation Using Fine-Tuning and Hyperparameter Optimization
Jim James, Nathan Pruyne, Tiberiu Stan +6
Dendritic microstructures are ubiquitous in nature and are the primary solidification morphologies in metallic materials. Techniques such as x-ray computed tomography (XCT) have pr…
KAISA: An Adaptive Second-Order Optimizer Framework for Deep Neural Networks
J. Gregory Pauloski, Qi Huang, Lei Huang +4
Kronecker-factored Approximate Curvature (K-FAC) has recently been shown to converge faster in deep neural network (DNN) training than stochastic gradient descent (SGD); however, K…
BFTrainer: Low-Cost Training of Neural Networks on Unfillable Supercomputer Nodes
Zhengchun Liu, Rajkumar Kettimuthu, Michael E. Papka +1
Supercomputer FCFS-based scheduling policies result in many transient idle nodes, a phenomenon that is only partially alleviated by backfill scheduling methods that promote small j…
Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder
Takuya Kurihana, Elisabeth Moyer, Rebecca Willett +2
Advanced satellite-born remote sensing instruments produce high-resolution multi-spectral data for much of the globe at a daily cadence. These datasets open up the possibility of i…