| Reversible Architectures for Arbitrarily Deep Residual Neural Networks B Chang, L Meng, E Haber, L Ruthotto, D Begert, E Holtham AAAI Conference on Artificial Intelligence (AAAI), 2018 | 248 | 2018 |
| AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks B Chang, M Chen, E Haber, EH Chi International Conference on Learning Representations (ICLR), 2019 | 173 | 2019 |
| Multi-level Residual Networks from Dynamical Systems View B Chang, L Meng, E Haber, F Tung, D Begert International Conference on Learning Representations (ICLR), 2018 | 155 | 2018 |
| Generating Handwritten Chinese Characters using CycleGAN B Chang, Q Zhang, S Pan, L Meng IEEE Winter Conference on Applications of Computer Vision (WACV), 2018 | 113 | 2018 |
| Interpretable spatio-temporal attention for video action recognition L Meng, B Zhao, B Chang, G Huang, W Sun, F Tung, L Sigal Proceedings of the IEEE International Conference on Computer Vision …, 2019 | 92* | 2019 |
| Modular Generative Adversarial Networks B Zhao, B Chang, Z Jie, L Sigal European Conference on Computer Vision (ECCV), 2018 | 81 | 2018 |
| Convolutional neural networks combined with Runge–Kutta methods M Zhu, B Chang, C Fu Neural Computing and Applications, 1-15, 2022 | 52 | 2022 |
| Prediction based on conditional distributions of vine copulas B Chang, H Joe Computational Statistics & Data Analysis 139, 45-63, 2019 | 44 | 2019 |
| Dynamical isometry and a mean field theory of LSTMs and GRUs D Gilboa, B Chang, M Chen, G Yang, SS Schoenholz, EH Chi, ... arXiv preprint arXiv:1901.08987, 2019 | 33 | 2019 |
| Modeling continuous stochastic processes with dynamic normalizing flows R Deng, B Chang, MA Brubaker, G Mori, A Lehrmann Conference on Neural Information Processing Systems (NeurIPS), 2020 | 30 | 2020 |
| User response models to improve a reinforce recommender system M Chen, B Chang, C Xu, EH Chi Proceedings of the 14th ACM International Conference on Web Search and Data …, 2021 | 22 | 2021 |
| Vine regression RM Cooke, H Joe, B Chang Resources for the Future Discussion Paper 15-52, 2015 | 17 | 2015 |
| Vine copula regression for observational studies RM Cooke, H Joe, B Chang AStA advances in statistical analysis 104, 141-167, 2020 | 13 | 2020 |
| Vine copula structure learning via Monte Carlo tree search B Chang, S Pan, H Joe International Conference on Artificial Intelligence and Statistics (AISTATS …, 2019 | 11 | 2019 |
| CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks J Ma, B Chang, X Zhang, Q Mei International Conference on Learning Representations (ICLR), 2021 | 10 | 2021 |
| Point process flows N Mehrasa, R Deng, MO Ahmed, B Chang, J He, T Durand, M Brubaker, ... arXiv preprint arXiv:1910.08281, 2019 | 10 | 2019 |
| Copula diagnostics for asymmetries and conditional dependence B Chang, H Joe Journal of Applied Statistics 47 (9), 1587-1615, 2020 | 3 | 2020 |
| Vine regression with Bayes nets: A critical comparison with traditional approaches based on a case study on the effects of breastfeeding on IQ RM Cooke, H Joe, B Chang Risk Analysis 42 (6), 1294-1305, 2022 | 2 | 2022 |
| Learning to Augment for Casual User Recommendation J Wang, Y Le, B Chang, Y Wang, EH Chi, M Chen Proceedings of the ACM Web Conference 2022, 2183-2194, 2022 | 2 | 2022 |
| Systems and methods for modeling continuous stochastic processes with dynamic normalizing flows D Ruizhi, B Chang, MA Brubaker, GP Mori, ASM Lehrmann US Patent App. 17/170,416, 2021 | 2 | 2021 |
Minmin ChenResearch scientist, GoogleVerified email at google.com
