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Found 10 papers, 4 papers with code
MLCommons Cloud Masking Benchmark with Early Stopping
We provide a description of the cloud masking benchmark, as well as a summary of our submission to MLCommons on the benchmark experiment we conducted.
Spatiotemporal Pattern Mining for Nowcasting Extreme Earthquakes in Southern California
With the enormous amount of data, modern GPU-powered deep learning presents a promising approach to analyze data and discover patterns.
CryptoGRU: Low Latency Privacy-Preserving Text Analysis With GRU
Although prior secure networks combine homomorphic encryption (HE) and garbled circuit (GC) to preserve users' privacy, naively adopting the HE and GC hybrid technique to implement RNNs suffers from long inference latency due to slow activation functions.
AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting with Population Covariates
The COVID-19 pandemic has profound global consequences on health, economic, social, political, and almost every major aspect of human life.
Solving Newton's Equations of Motion with Large Timesteps using Recurrent Neural Networks based Operators
Classical molecular dynamics simulations are based on solving Newton's equations of motion.
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data
Big data is one of the cornerstones to enabling and training deep neural networks (DNNs).
Parallel Performance of Molecular Dynamics Trajectory Analysis
The performance of biomolecular molecular dynamics simulations has steadily increased on modern high performance computing resources but acceleration of the analysis of the output trajectories has lagged behind so that analyzing simulations is becoming a bottleneck.
Distributed, Parallel, and Cluster Computing Quantitative Methods D.1.3; J.2
Task-parallel Analysis of Molecular Dynamics Trajectories
We also provide a quantitative performance analysis of the different algorithms across the three frameworks.
Distributed, Parallel, and Cluster Computing
Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction
Deep learning methods have surpassed the performance of traditional techniques on a wide range of problems in computer vision, but nearly all of this work has studied consumer photos, where precisely correct output is often not critical.
Automatic Estimation of Ice Bottom Surfaces from Radar Imagery
Ground-penetrating radar on planes and satellites now makes it practical to collect 3D observations of the subsurface structure of the polar ice sheets, providing crucial data for understanding and tracking global climate change.
