| Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN) Y Alanazi, N Sato, T Liu, W Melnitchouk, P Ambrozewicz, F Hauenstein, ... https://www.ijcai.org/proceedings/2021/293, 2020 | 71 | 2020 |
| A survey of machine learning-based physics event generation Y Alanazi, N Sato, P Ambrozewicz, ANH Blin, W Melnitchouk, ... https://www.ijcai.org/proceedings/2021/588, 2021 | 24 | 2021 |
| Variational autoencoder inverse mapper: An end-to-end deep learning framework for inverse problems M Almaeen, Y Alanazi, N Sato, W Melnitchouk, MP Kuchera, Y Li 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021 | 20 | 2021 |
| Machine learning-based event generator for electron-proton scattering Y Alanazi, P Ambrozewicz, M Battaglieri, AN Hiller Blin, MP Kuchera, Y Li, ... Physical Review D 106 (9), 096002, 2022 | 17* | 2022 |
| cFAT-GAN: Conditional Simulation of Electron–Proton Scattering Events with Variate Beam Energies by a Feature Augmented and Transformed Generative Adversarial Network L Velasco, E McClellan, N Sato, P Ambrozewicz, T Liu, W Melnitchouk, ... Deep Learning Applications, Volume 3, 245-261, 2022 | 13 | 2022 |
| Point cloud-based variational autoencoder inverse mappers (pc-vaim)-an application on quantum chromodynamics global analysis M Almaeen, Y Alanazi, N Sato, W Melnitchouk, Y Li 2022 21st IEEE International Conference on Machine Learning and Applications …, 2022 | 7 | 2022 |
| Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence Y Alanazi, N Sato, T Liu, W Melnitchouk, P Ambrozewicz, F Hauenstein, ... International Joint Conferences on Artificial Intelligence Organization, 2021 | 6 | 2021 |
| Robust errant beam prognostics with conditional modeling for particle accelerators K Rajput, M Schram, W Blokland, Y Alanazi, P Ramuhalli, A Zhukov, ... Machine Learning: Science and Technology 5 (1), 015044, 2024 | 5 | 2024 |
| Toward a generative modeling analysis of CLAS exclusive photoproduction T Alghamdi, Y Alanazi, M Battaglieri, Ł Bibrzycki, AV Golda, AN Hiller Blin, ... Physical Review D 108 (9), 094030, 2023 | 4 | 2023 |
| Multi-module-based CVAE to predict HVCM faults in the SNS accelerator Y Alanazi, M Schram, K Rajput, S Goldenberg, L Vidyaratne, C Pappas, ... Machine Learning with Applications 13, 100484, 2023 | 4 | 2023 |
| Investigating anomalies in compute clusters: An unsupervised learning approach Y Lu, J Ren, Y Alanazi, A Mohammed, D McSpadden, L Hild, M Jones, ... SC23 2, 2024 | 1 | 2024 |
| Dataset for Investigating Anomalies in Compute Clusters D McSpadden, Y Alanazi, B Hess, L Hild, M Jones, Y Lub, A Mohammed, ... arXiv preprint arXiv:2311.16129, 2023 | 1 | 2023 |
| Errant Beam Prognostics with Machine Leaning at SNS Accelerator K Rajput, M Schram, W Blokland, Y Alanazi, P Ramuhalli, A Zhukov, ... Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA …, 2024 | | 2024 |
| Machine Learning-Based Event Generator Y Alanazi Old Dominion University, 2022 | | 2022 |
| VAIM for Solving Inverse Problems M Almaeen, Y Alanazi, M Kuchera, N Sato, W Melnitchouk, Y Li | | 2021 |
| End-to-end physics event generator Y Alanazi, N Sato, T Liu, W Melnitchouk, MP Kuchera, E Pritchard, ... | | 2021 |
| Application of Generative Adverserial Networks to electron-proton scattering P Ambrozewicz, Y Alanazi, M Kuchera, Y Li, T Liu, E McClellan, ... APS April Meeting Abstracts 2021, H15. 006, 2021 | | 2021 |
| Using machine learning techniques to interface between experimental cross sections and QCD theory parameters E Tsitinidi, R Shahid, Y Alanazi, M Almaeen, M Kuchera, Y Li, ... APS Division of Nuclear Physics Meeting Abstracts 2020, HA. 005, 2020 | | 2020 |
| Using neural networks to generate cross section data from theoretical QCD parameters R Shahid, E Tsitinidi, Y Alanazi, M Almaeen, M Kuchera, Y Li, ... APS Division of Nuclear Physics Meeting Abstracts 2020, JA. 005, 2020 | | 2020 |
| Machine learning methods for predictions in the future Electron-Ion Collider MP Kuchera, Y Alanazi, M Almaeen, M Houck, T Liu, E McClellan, ... APS Division of Nuclear Physics Meeting Abstracts 2019, FE. 004, 2019 | | 2019 |
