| Jet-images—deep learning edition L de Oliveira, M Kagan, L Mackey, B Nachman, A Schwartzman Journal of High Energy Physics 2016 (7), 1-32, 2016 | 371 | 2016 |
| CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks M Paganini, L de Oliveira, B Nachman Physical Review D 97 (1), 014021, 2018 | 307 | 2018 |
| Learning particle physics by example: location-aware generative adversarial networks for physics synthesis L de Oliveira, M Paganini, B Nachman Computing and Software for Big Science 1 (1), 4, 2017 | 285 | 2017 |
| Accelerating science with generative adversarial networks: an application to 3D particle showers in multilayer calorimeters M Paganini, L de Oliveira, B Nachman Physical review letters 120 (4), 042003, 2018 | 223 | 2018 |
| Controlling physical attributes in GAN-accelerated simulation of electromagnetic calorimeters L De Oliveira, M Paganini, B Nachman Journal of Physics: Conference Series 1085 (4), 042017, 2018 | 42 | 2018 |
| Electromagnetic showers beyond shower shapes L De Oliveira, B Nachman, M Paganini Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 2020 | 36 | 2020 |
| Image Processing, Computer Vision, and Deep Learning: new approaches to the analysis and physics interpretation of LHC events A Schwartzman, M Kagan, L Mackey, B Nachman, L De Oliveira Journal of Physics: Conference Series 762 (1), 012035, 2016 | 32 | 2016 |
| Humor detection in yelp reviews L De Oliveira, AL Rodrigo Retrieved on December 15, 2019, 2015 | 22 | 2015 |
| Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis, Comput. Softw. Big Sci. 1 (2017) 1, 4 L de Oliveira, M Paganini, B Nachman arXiv preprint arXiv:1701.05927, 0 | 15 | |
| Tips and tricks for training gans with physics constraints MP Luke de Oliveira, B Nachman Workshop at the 31st Conference on Neural Information Processing Systems …, 2017 | 7* | 2017 |
| Boosted jet tagging with jet-images and deep neural networks M Kagan, L de Oliveira, L Mackey, B Nachman, A Schwartzman EPJ Web of Conferences 127, 00009, 2016 | 7 | 2016 |
| CodeDroid: a framework to develop context-aware applications L de Oliveira, A Loureiro The Fourth International Conferences on Advances in Human-oriented and …, 2011 | 6 | 2011 |
| HEP Software Foundation Community White Paper Working Group-Detector Simulation HEP Foundation, J Apostolakis, M Asai, S Banerjee, R Bianchi, P Canal, ... arXiv preprint arXiv:1803.04165, 2018 | 5* | 2018 |
| Survey of machine learning techniques for high energy electromagnetic shower classification M Paganini, L de Oliveira, B Nachman Deep Learning for Physical Sciences Workshop at the 31st Conference on …, 2017 | 4 | 2017 |
| Repurposing decoder-transformer language models for abstractive summarization L de Oliveira, AL Rodrigo arXiv preprint arXiv:1909.00325, 2019 | 3 | 2019 |
| Transition-driven search LP De Oliveira, U Akeel, AL Rodrigo, NA Amador, S Kumar, LB Dremer, ... US Patent App. 17/305,976, 2022 | 1 | 2022 |
| Tool for categorizing and extracting data from audio conversations AL Rodrigo, T Cole, U Akeel, LP De Oliveira US Patent App. 17/449,405, 2022 | 1 | 2022 |
| Generative Models for Fast Simulation M Paganini, L de Oliveira, B Nachman, D Derkach, F Ratnikov, ... Artificial Intelligence For High Energy Physics, 153-189, 2022 | 1 | 2022 |
| Language model for abstractive summarization LP De Oliveira, AL Rodrigo US Patent App. 17/939,176, 2022 | | 2022 |
| Text formatter AL Rodrigo, LP De Oliveira, U Akeel, T Cole US Patent App. 17/303,279, 2022 | | 2022 |
Benjamin NachmanStaff Scientist, Lawrence Berkeley National LaboratoryVerified email at lbl.gov
