Search Results for author: Zois Boukouvalas

Found 10 papers, 2 papers with code

A Semi-Supervised Framework for Misinformation Detection

no code implementations22 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.


Assessing the trade-off between prediction accuracy and interpretability for topic modeling on energetic materials corpora

no code implementations1 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.

Document Embedding

Deep learning for molecular design - a review of the state of the art

no code implementations11 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.

Benchmarking reinforcement-learning +1

Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora

no code implementations1 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.

Word Embeddings

Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning

1 code implementation1 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.

BIG-bench Machine Learning Drug Discovery +2

Machine Learning of Energetic Material Properties

2 code implementations17 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

Development of ICA and IVA Algorithms with Application to Medical Image Analysis

no code implementations25 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.

Independent Component Analysis by Entropy Maximization with Kernels

no code implementations22 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.

Enhancing ICA Performance by Exploiting Sparsity: Application to FMRI Analysis

no code implementations19 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.

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