| Mode regularized generative adversarial networks T Che, Y Li, AP Jacob, Y Bengio, W Li arXiv preprint arXiv:1612.02136, 2016 | 731 | 2016 |
| Metagan: An adversarial approach to few-shot learning R Zhang, T Che, Z Ghahramani, Y Bengio, Y Song Advances in neural information processing systems 31, 2018 | 701 | 2018 |
| Maximum-likelihood augmented discrete generative adversarial networks T Che, Y Li, R Zhang, RD Hjelm, W Li, Y Song, Y Bengio arXiv preprint arXiv:1702.07983, 2017 | 320 | 2017 |
| Boundary-seeking generative adversarial networks RD Hjelm, AP Jacob, T Che, A Trischler, K Cho, Y Bengio arXiv preprint arXiv:1702.08431, 2017 | 232 | 2017 |
| Architectural complexity measures of recurrent neural networks S Zhang, Y Wu, T Che, Z Lin, R Memisevic, RR Salakhutdinov, Y Bengio Advances in neural information processing systems 29, 2016 | 199 | 2016 |
| Residual connections encourage iterative inference S Jastrzębski, D Arpit, N Ballas, V Verma, T Che, Y Bengio arXiv preprint arXiv:1710.04773, 2017 | 160 | 2017 |
| Guided conditional diffusion for controllable traffic simulation Z Zhong, D Rempe, D Xu, Y Chen, S Veer, T Che, B Ray, M Pavone 2023 IEEE International Conference on Robotics and Automation (ICRA), 3560-3566, 2023 | 135 | 2023 |
| Your GAN is secretly an energy-based model and you should use discriminator driven latent sampling T Che, R Zhang, J Sohl-Dickstein, H Larochelle, L Paull, Y Cao, Y Bengio Advances in Neural Information Processing Systems 33, 12275-12287, 2020 | 132 | 2020 |
| Deep verifier networks: Verification of deep discriminative models with deep generative models T Che, X Liu, S Li, Y Ge, R Zhang, C Xiong, Y Bengio Proceedings of the AAAI conference on artificial intelligence 35 (8), 7002-7010, 2021 | 67 | 2021 |
| Energy-based open-world uncertainty modeling for confidence calibration Y Wang, B Li, T Che, K Zhou, Z Liu, D Li Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 66 | 2021 |
| Sparse mixture-of-experts are domain generalizable learners B Li, Y Shen, J Yang, Y Wang, J Ren, T Che, J Zhang, Z Liu arXiv preprint arXiv:2206.04046, 2022 | 64 | 2022 |
| Rethinking distributional matching based domain adaptation B Li, Y Wang, T Che, S Zhang, S Zhao, P Xu, W Zhou, Y Bengio, ... arXiv preprint arXiv:2006.13352, 2020 | 63 | 2020 |
| Learning from teaching regularization: Generalizable correlations should be easy to imitate C Jin, T Che, H Peng, Y Li, DN Metaxas, M Pavone arXiv preprint arXiv:2402.02769, 2024 | 46 | 2024 |
| Emernerf: Emergent spatial-temporal scene decomposition via self-supervision J Yang, B Ivanovic, O Litany, X Weng, SW Kim, B Li, T Che, D Xu, S Fidler, ... arXiv preprint arXiv:2311.02077, 2023 | 39 | 2023 |
| Conservative wasserstein training for pose estimation X Liu, Y Zou, T Che, P Ding, P Jia, J You, BVK Kumar Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 37 | 2019 |
| Auto3d: Novel view synthesis through unsupervisely learned variational viewpoint and global 3d representation X Liu, T Che, Y Lu, C Yang, S Li, J You European Conference on Computer Vision, 52-71, 2020 | 26 | 2020 |
| Llama-berry: Pairwise optimization for o1-like olympiad-level mathematical reasoning D Zhang, J Wu, J Lei, T Che, J Li, T Xie, X Huang, S Zhang, M Pavone, ... arXiv preprint arXiv:2410.02884, 2024 | 21 | 2024 |
| Designing ascy-compliant concurrent search data structures TA David, R Guerraoui, T Che, V Trigonakis | 14 | 2014 |
| Robust and controllable object-centric learning through energy-based models R Zhang, T Che, B Ivanovic, R Wang, M Pavone, Y Bengio, L Paull arXiv preprint arXiv:2210.05519, 2022 | 11 | 2022 |
| Foundation models for semantic novelty in reinforcement learning T Gupta, P Karkus, T Che, D Xu, M Pavone arXiv preprint arXiv:2211.04878, 2022 | 7 | 2022 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARZweryfikowany adres z umontreal.ca
