4 citations · 10 across the 7 of their papers we have counts for
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Prediction of Runtime Parameters of Parallel Chemistry Applications via Active and Generative Learning
Tanzila Tabassum, Omer Subasi, Ajay Panyala +5
In this work, we develop two main Machine Learning based approaches to predict the runtime parameters of highly scalable parallel chemistry computations.These approaches employ act…
Guiding Application Users via Estimation of Computational Resources for Massively Parallel Chemistry Computations
Tanzila Tabassum, Omer Subasi, Ajay Panyala +6
In this work, we develop machine learning (ML) based strategies to predict resources (costs) required for massively parallel chemistry computations, such as coupled-cluster methods…
The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning
Omer Subasi, Oceane Bel, Joseph Manzano +1
With the advance of the powerful heterogeneous, parallel and distributed computing systems and ever increasing immense amount of data, machine learning has become an indispensable…
Toward Automated Quantum Variational Machine Learning
Omer Subasi
In this work, we address the problem of automating quantum variational machine learning. We develop a multi-locality parallelizable search algorithm, called MUSE, to find the initi…