Gerry Che
Gerry Che
Nvidia Research
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Cited by
Cited by
Mode regularized generative adversarial networks
T Che, Y Li, AP Jacob, Y Bengio, W Li
arXiv preprint arXiv:1612.02136, 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
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
Boundary-seeking generative adversarial networks
RD Hjelm, AP Jacob, T Che, A Trischler, K Cho, Y Bengio
arXiv preprint arXiv:1702.08431, 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
Residual connections encourage iterative inference
S Jastrzębski, D Arpit, N Ballas, V Verma, T Che, Y Bengio
arXiv preprint arXiv:1710.04773, 2017
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
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
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
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
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
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
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
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
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
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
Designing ascy-compliant concurrent search data structures
TA David, R Guerraoui, T Che, V Trigonakis
Combining model-based and model-free RL via multi-step control variates
T Che, Y Lu, G Tucker, S Bhupatiraju, S Gu, S Levine, Y Bengio
spe: symmetrical prompt enhancement for fact probing
Y Li, T Che, Y Wang, Z Jiang, C Xiong, S Chaturvedi
Foundation models for semantic novelty in reinforcement learning
T Gupta, P Karkus, T Che, D Xu, M Pavone
arXiv preprint arXiv:2211.04878, 2022
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