120 citations · 223 across the 24 of their papers we have counts for
4 papers · 1 filter
Scientific Machine Learning Benchmarks
Jeyan Thiyagalingam, Mallikarjun Shankar, Geoffrey Fox +1
The breakthrough in Deep Learning neural networks has transformed the use of AI and machine learning technologies for the analysis of very large experimental datasets. These datase…
Investigating Deep Learning Methods for Obtaining Photometric Redshift Estimations from Images
Ben Henghes, Connor Pettitt, Jeyan Thiyagalingam +2
Knowing the redshift of galaxies is one of the first requirements of many cosmological experiments, and as it's impossible to perform spectroscopy for every galaxy being observed,…
Entropy-based Active Learning of Graph Neural Network Surrogate Models for Materials Properties
Johannes Allotey, Keith T. Butler, Jeyan Thiyagalingam
Graph neural networks, trained on experimental or calculated data are becoming an increasingly important tool in computational materials science. Networks, once trained, are able t…
Benchmarking and Scalability of Machine Learning Methods for Photometric Redshift Estimation
Ben Henghes, Connor Pettitt, Jeyan Thiyagalingam +2
Obtaining accurate photometric redshift estimations is an important aspect of cosmology, remaining a prerequisite of many analyses. In creating novel methods to produce redshift es…