Ben Nebgen

According to our database, Ben Nebgen authored at least 11 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Topological Signatures of Adversaries in Multimodal Alignments.

Minh N. Vu, Geigh Zollicoffer, Huy Mai, Ben Nebgen, Boian S. Alexandrov, Manish Bhattarai

CoRR, January, 2025

Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation.

Luan G. F. dos Santos, Benjamin T. Nebgen, Alice E. A. Allen, Brenden W. Hamilton, Sakib Matin, Justin S. Smith, Richard A. Messerly

J. Chem. Inf. Model., 2025

LoRID: Low-Rank Iterative Diffusion for Adversarial Purification.

Geigh Zollicoffer, Minh N. Vu, Ben Nebgen, Juan Castorena, Boian S. Alexandrov, Manish Bhattarai

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

LaFA: Latent Feature Attacks on Non-negative Matrix Factorization.

Minh N. Vu, Ben Nebgen, Erik Skau, Geigh Zollicoffer, Juan Castorena, Kim Ø. Rasmussen, Boian S. Alexandrov, Manish Bhattarai

CoRR, 2024

2023

Distributed non-negative RESCAL with automatic model selection for exascale data.

Manish Bhattarai, Namita Kharat, Ismael Boureima, Erik Skau, Benjamin T. Nebgen, Hristo N. Djidjev, Sanjay V. Rajopadhye, James P. Smith, Boian S. Alexandrov

J. Parallel Distributed Comput., September, 2023

Uncertainty-driven dynamics for active learning of interatomic potentials.

Maksim Kulichenko, Kipton Barros, Nicholas Lubbers, Ying Wai Li, Richard A. Messerly, Sergei Tretiak, Justin S. Smith, Benjamin T. Nebgen

Nat. Comput. Sci., 2023

Robust Adversarial Defense by Tensor Factorization.

Manish Bhattarai, Mehmet Cagri Kaymak, Ryan Barron, Ben Nebgen, Kim Ø. Rasmussen, Boian S. Alexandrov

Proceedings of the International Conference on Machine Learning and Applications, 2023

2022

Distributed non-negative RESCAL with Automatic Model Selection for Exascale Data.

Manish Bhattarai, Namita Kharat, Erik Skau, Benjamin T. Nebgen, Hristo N. Djidjev, Sanjay V. Rajopadhye, James P. Smith, Boian S. Alexandrov

CoRR, 2022

2020

A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization.

Benjamin T. Nebgen, Raviteja Vangara, Miguel Ángel Hombrados-Herrera, Svetlana Kuksova, Boian S. Alexandrov

CoRR, 2020

Automated discovery of a robust interatomic potential for aluminum.

Justin S. Smith, Benjamin T. Nebgen, Nithin Mathew, Jie Chen, Nicholas Lubbers, Leonid Burakovsky, Sergei Tretiak, Hai Ah Nam, Timothy C. Germann, Saryu Fensin, Kipton Barros

CoRR, 2020

2018

Less is more: sampling chemical space with active learning.

Justin S. Smith, Ben Nebgen, Nicholas Lubbers, Olexandr Isayev, Adrian E. Roitberg

CoRR, 2018