no code implementations • 22 Apr 2023 • Yueyang Liu, Zois Boukouvalas, Nathalie Japkowicz
The spread of misinformation in social media outlets has become a prevalent societal problem and is the cause of many kinds of social unrest.
no code implementations • 1 Jun 2022 • Monica Puerto, Mason Kellett, Rodanthi Nikopoulou, Mark D. Fuge, Ruth Doherty, Peter W. Chung, Zois Boukouvalas
With our accuracy results, we also introduce local interpretability model-agnostic explanations (LIME) of each prediction to provide a localized understanding of each prediction and to validate classifier decisions with our team of energetics experts.
no code implementations • 1 Jun 2020 • Zois Boukouvalas, Christine Mallinson, Evan Crothers, Nathalie Japkowicz, Aritran Piplai, Sudip Mittal, Anupam Joshi, Tülay Adalı
Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic.
no code implementations • 11 Mar 2019 • Daniel C. Elton, Zois Boukouvalas, Mark D. Fuge, Peter W. Chung
In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, music, and text.
no code implementations • 1 Mar 2019 • Daniel C. Elton, Dhruv Turakhia, Nischal Reddy, Zois Boukouvalas, Mark D. Fuge, Ruth M. Doherty, Peter W. Chung
The number of scientific journal articles and reports being published about energetic materials every year is growing exponentially, and therefore extracting relevant information and actionable insights from the latest research is becoming a considerable challenge.
1 code implementation • 1 Nov 2018 • Zois Boukouvalas, Daniel C. Elton, Peter W. Chung, Mark D. Fuge
Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has received great attention in the fields of materials design and drug discovery.
2 code implementations • 17 Jul 2018 • Brian C. Barnes, Daniel C. Elton, Zois Boukouvalas, DeCarlos E. Taylor, William D. Mattson, Mark D. Fuge, Peter W. Chung
In this work, we discuss use of machine learning techniques for rapid prediction of detonation properties including explosive energy, detonation velocity, and detonation pressure.
Materials Science Chemical Physics Computational Physics
no code implementations • 25 Jan 2018 • Zois Boukouvalas
In this work, we first introduce a flexible ICA algorithm that uses an effective PDF estimator to accurately capture the underlying statistical properties of the data.
no code implementations • 22 Oct 2016 • Zois Boukouvalas, Rami Mowakeaa, Geng-Shen Fu, Tulay Adali
ICA algorithms cast in the ML framework often deviate from its theoretical optimality properties due to poor estimation of the source PDF.
no code implementations • 19 Oct 2016 • Zois Boukouvalas, Yuri Levin-Schwartz, Tulay Adali
Independent component analysis (ICA) is a powerful method for blind source separation based on the assumption that sources are statistically independent.