115 citations · 179 across the 14 of their papers we have counts for
6 papers · 1 filter
Scientific Image Restoration Anywhere
Vibhatha Abeykoon, Zhengchun Liu, Rajkumar Kettimuthu +2
The use of deep learning models within scientific experimental facilities frequently requires low-latency inference, so that, for example, quality control operations can be perform…
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data
Qian Lou, Bo Feng, Geoffrey C. Fox +1
Big data is one of the cornerstones to enabling and training deep neural networks (DNNs). Because of the lack of expertise, to gain benefits from their data, average users have to…
Machine Learning for Parameter Auto-tuning in Molecular Dynamics Simulations: Efficient Dynamics of Ions near Polarizable Nanoparticles
JCS Kadupitiya, Geoffrey C. Fox, Vikram Jadhao
Simulating the dynamics of ions near polarizable nanoparticles (NPs) using coarse-grained models is extremely challenging due to the need to solve the Poisson equation at every sim…
Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations
Geoffrey Fox, Shantenu Jha
We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities. We cover eight patterns for the link of ML to…
Parallel Performance of Molecular Dynamics Trajectory Analysis
Mahzad Khoshlessan, Ioannis Paraskevakos, Geoffrey C. Fox +2
The performance of biomolecular molecular dynamics simulations has steadily increased on modern high performance computing resources but acceleration of the analysis of the output…
Learning Everywhere: Pervasive Machine Learning for Effective High-Performance Computation
Geoffrey Fox, James A. Glazier, JCS Kadupitiya +10
The convergence of HPC and data-intensive methodologies provide a promising approach to major performance improvements. This paper provides a general description of the interaction…