| The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics G Kasieczka, B Nachman, D Shih, O Amram, A Andreassen, ... Reports on progress in physics 84 (12), 124201, 2021 | 155 | 2021 |
| Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian deep learning B Dai, U Seljak Proceedings of the National Academy of Sciences 118 (16), e2020324118, 2021 | 43 | 2021 |
| Sliced iterative normalizing flows B Dai, U Seljak arXiv preprint arXiv:2007.00674, 2020 | 39 | 2020 |
| A gradient based method for modeling baryons and matter in halos of fast simulations B Dai, Y Feng, U Seljak Journal of Cosmology and Astroparticle Physics 2018 (11), 009, 2018 | 35 | 2018 |
| Translation and rotation equivariant normalizing flow (TRENF) for optimal cosmological analysis B Dai, U Seljak Monthly Notices of the Royal Astronomical Society 516 (2), 2363-2373, 2022 | 30 | 2022 |
| Unsupervised in-distribution anomaly detection of new physics through conditional density estimation G Stein, U Seljak, B Dai arXiv preprint arXiv:2012.11638, 2020 | 30 | 2020 |
| Around the Way: Testing ΛCDM with Milky Way Stellar Stream Constraints B Dai, BE Robertson, P Madau The Astrophysical Journal 858 (2), 73, 2018 | 20 | 2018 |
| MADLens, a python package for fast and differentiable non-Gaussian lensing simulations V Böhm, Y Feng, ME Lee, B Dai Astronomy and Computing 36, 100490, 2021 | 18 | 2021 |
| High mass and halo resolution from fast low resolution simulations B Dai, Y Feng, U Seljak, S Singh Journal of Cosmology and Astroparticle Physics 2020 (04), 002, 2020 | 10 | 2020 |
| Unsupervised in-distribution anomaly detection of new physics through conditional density estimation,(2020) G Stein, U Seljak, B Dai arXiv preprint arXiv:2012.11638, 0 | 5 | |
| Deterministic Langevin Monte Carlo with normalizing flows for Bayesian inference R Grumitt, B Dai, U Seljak Advances in Neural Information Processing Systems 35, 11629-11641, 2022 | 4 | 2022 |
| Sliced iterative generator B Dai, U Seljak arXiv preprint arXiv:2007.00674, 2020 | 3 | 2020 |
| Multiscale Flow for robust and optimal cosmological analysis B Dai, U Seljak Proceedings of the National Academy of Sciences 121 (9), e2309624121, 2024 | 2 | 2024 |
| A comparative study of cosmological constraints from weak lensing using Convolutional Neural Networks D Sharma, B Dai, U Seljak arXiv preprint arXiv:2403.03490, 2024 | | 2024 |
| A field-level emulator for modeling baryonic effects across hydrodynamic simulations D Sharma, B Dai, F Villaescusa-Navarro, U Seljak arXiv preprint arXiv:2401.15891, 2024 | | 2024 |
| arXiv: Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning P Shanahan, O Amram, JF Kamenik, A Matevc, A Gandrakota, B Lucini, ... | | 2022 |
| Normalizing Flows with Translational and Rotational Symmetry for Optimal Cosmological Analysis B Dai, U Seljak American Astronomical Society Meeting Abstracts 53 (6), 103.03, 2021 | | 2021 |
| The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics O Amram, A Andreassen, K Benkendorfer, B Bortolato, G Brooijmans, ... ArXivorg, 2021 | | 2021 |
| MADLens: Differentiable lensing simulator V Böhm, Y Feng, ME Lee, B Dai Astrophysics Source Code Library, ascl: 2012.010, 2020 | | 2020 |
| On the Shape of Dark Matter Halos in Milky Way-like Galaxies B Dai, BE Robertson, P Madau American Astronomical Society Meeting Abstracts# 229 229, 342.10, 2017 | | 2017 |
