25 citations · 58 across the 9 of their papers we have counts for
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cond-mat.mtrl-sci2023
Evaluating the diversity and utility of materials proposed by generative models
Alexander New, Michael Pekala, Elizabeth A. Pogue +4
Generative machine learning models can use data generated by scientific modeling to create large quantities of novel material structures. Here, we assess how one state-of-the-art g…
cs.LG2023★ 17 cited
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…