| An empirical evaluation of generic convolutional and recurrent networks for sequence modeling S Bai, JZ Kolter, V Koltun International Conference on Learning Representations (ICLR) Workshop, 2018 | 7237 | 2018 |
| Multimodal Transformer for Unaligned Multimodal Language Sequences YHH Tsai*, S Bai*, PP Liang, JZ Kolter, LP Morency, R Salakhutdinov Proceedings of the Annual Meeting of the Association for Computational …, 2019 | 1677 | 2019 |
| Deep equilibrium models S Bai, JZ Kolter, V Koltun Advances in Neural Information Processing Systems, 688-699, 2019 | 808 | 2019 |
| Transformer Dissection: An Unified Understanding for Transformer's Attention via the Lens of Kernel YHH Tsai, S Bai, M Yamada, LP Morency, R Salakhutdinov Proceedings of the Conference on Empirical Methods in Natural Language …, 2019 | 309 | 2019 |
| Multiscale Deep Equilibrium Models S Bai, V Koltun, JZ Kolter Advances in Neural Information Processing Systems, 2020 | 246 | 2020 |
| Trellis networks for sequence modeling S Bai, JZ Kolter, V Koltun International Conference on Learning Representations (ICLR), 2019 | 178 | 2019 |
| On Training Implicit Models Z Geng*, XY Zhang*, S Bai, Y Wang, Z Lin Advances in Neural Information Processing Systems 34, 2021 | 83 | 2021 |
| Deep Equilibrium Optical Flow Estimation S Bai, Z Geng, Y Savani, JZ Kolter Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 82 | 2022 |
| Convolutional sequence modeling revisited S Bai, JZ Kolter, V Koltun International Conference on Learning Representations (ICLR), 2018 | 78 | 2018 |
| Stabilizing Equilibrium Models by Jacobian Regularization S Bai, V Koltun, JZ Kolter International Conference on Machine Learning (ICML), 2021 | 71 | 2021 |
| Neural deep equilibrium solvers S Bai, V Koltun, JZ Kolter International Conference on Learning Representations, 2021 | 47 | 2021 |
| : Implicit Layers for Implicit Representations Z Huang, S Bai, JZ Kolter Advances in neural information processing systems 34, 9639-9650, 2021 | 46 | 2021 |
| A note on connecting barlow twins with negative-sample-free contrastive learning YHH Tsai, S Bai, LP Morency, R Salakhutdinov arXiv preprint arXiv:2104.13712, 2021 | 46 | 2021 |
| From audio to photoreal embodiment: Synthesizing humans in conversations E Ng, J Romero, T Bagautdinov, S Bai, T Darrell, A Kanazawa, A Richard Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 38 | 2024 |
| SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models Z Ramzi, F Mannel, S Bai, JL Starck, P Ciuciu, T Moreau International Conference on Learning Representations (ICLR), 2022 | 35 | 2022 |
| AHEAD: Adaptive hierarchical decomposition for range query under local differential privacy L Du, Z Zhang, S Bai, C Liu, S Ji, P Cheng, J Chen Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications …, 2021 | 33 | 2021 |
| Path Independent Equilibrium Models Can Better Exploit Test-Time Computation C Anil, A Pokle, K Liang, J Treutlein, Y Wu, S Bai, Z Kolter, R Grosse Advances in Neural Information Processing Systems, 2022 | 25 | 2022 |
| Joint inference and input optimization in equilibrium networks S Gurumurthy, S Bai, Z Manchester, JZ Kolter Advances in Neural Information Processing Systems 34, 16818-16832, 2021 | 20 | 2021 |
| A community-powered search of machine learning strategy space to find NMR property prediction models LA Bratholm, W Gerrard, B Anderson, S Bai, S Choi, L Dang, P Hanchar, ... PLOS One 16 (7), e0253612, 2021 | 17 | 2021 |
| An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. March 2018 S Bai, JZ Kolter, V Koltun | 14 | 2019 |
Zico KolterCarnegie Mellon UniversityVerified email at cs.cmu.edu
