| 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 | 311 | 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 | 256 | 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 | 190 | 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 | 176 | 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 | 119* | 2019 |
| Modular Generative Adversarial Networks B Zhao, B Chang, Z Jie, L Sigal European Conference on Computer Vision (ECCV), 2018 | 87 | 2018 |
| Convolutional neural networks combined with Runge–Kutta methods M Zhu, B Chang, C Fu Neural Computing and Applications, 1-15, 2022 | 72 | 2022 |
| Prediction based on conditional distributions of vine copulas B Chang, H Joe Computational Statistics & Data Analysis 139, 45-63, 2019 | 63 | 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 | 60 | 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 | 58 | 2021 |
| 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 | 40 | 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 | 23 | 2021 |
| Vine copula regression for observational studies RM Cooke, H Joe, B Chang AStA advances in statistical analysis 104, 141-167, 2020 | 18 | 2020 |
| Vine regression RM Cooke, H Joe, B Chang Resources for the Future Discussion Paper 15-52, 2015 | 18 | 2015 |
| Point process flows N Mehrasa, R Deng, MO Ahmed, B Chang, J He, T Durand, M Brubaker, ... arXiv preprint arXiv:1910.08281, 2019 | 15 | 2019 |
| Vine copula structure learning via Monte Carlo tree search B Chang, S Pan, H Joe International Conference on Artificial Intelligence and Statistics (AISTATS …, 2019 | 14 | 2019 |
| Latent user intent modeling for sequential recommenders B Chang, A Karatzoglou, Y Wang, C Xu, EH Chi, M Chen Companion Proceedings of the ACM Web Conference 2023, 427-431, 2023 | 12 | 2023 |
| 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 | 12 | 2022 |
| Copula diagnostics for asymmetries and conditional dependence B Chang, H Joe Journal of Applied Statistics 47 (9), 1587-1615, 2020 | 4 | 2020 |
| Cluster Anchor Regularization to Alleviate Popularity Bias in Recommender Systems B Chang, C Meng, H Ma, S Chang, Y Gu, Y Peng, J Feng, Y Zhang, S Bi, ... Companion Proceedings of the ACM on Web Conference 2024, 151-160, 2024 | 3 | 2024 |
Minmin ChenResearch scientist, Google DeepmindVerified email at google.com
