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ORCIDTongzhou Wang 0001
Person information
- affiliation: Massachusetts Institute of Technology, MA, USA
- affiliation: Facebook AI Research, USA
- affiliation: University of California, Berkeley, CA, USA
Other persons with the same name
- Tongzhou Wang 0002 — Beijing University of Posts and Telecommunications, Beijing, China
- Tongzhou Wang 0003 — Shanghai University of Engineering Science, School of Mechanical and Automotive Engineering, China
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Conference and Workshop Papers
- 2024
[c16]Minyoung Huh, Brian Cheung, Tongzhou Wang, Phillip Isola:
Position: The Platonic Representation Hypothesis. ICML 2024- 2023
- [c15]George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, Jun-Yan Zhu:
Generalizing Dataset Distillation via Deep Generative Prior. CVPR 2023: 3739-3748 - [c14]Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang:
Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning. ICML 2023: 36411-36430 - 2022
- [c13]George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, Jun-Yan Zhu:
Wearable ImageNet: Synthesizing Tileable Textures via Dataset Distillation. CVPR Workshops 2022: 2277-2281 - [c12]George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, Jun-Yan Zhu:
Dataset Distillation by Matching Training Trajectories. CVPR Workshops 2022: 4749-4758 - [c11]George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, Jun-Yan Zhu:
Dataset Distillation by Matching Training Trajectories. CVPR 2022: 10708-10717 - [c10]Jingwei Ma
, Lucy Chai, Minyoung Huh, Tongzhou Wang, Ser-Nam Lim, Phillip Isola, Antonio Torralba:
Totems: Physical Objects for Verifying Visual Integrity. ECCV (14) 2022: 164-180 - [c9]Tongzhou Wang, Phillip Isola:
On the Learning and Learnability of Quasimetrics. ICLR 2022 - [c8]Tongzhou Wang, Simon S. Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian:
Denoised MDPs: Learning World Models Better Than the World Itself. ICML 2022: 22591-22612 - [c7]Manel Baradad, Chun-Fu Richard Chen, Jonas Wulff, Tongzhou Wang, Rogério Feris, Antonio Torralba, Phillip Isola:
Procedural Image Programs for Representation Learning. NeurIPS 2022 - 2021
- [c6]Manel Baradad Jurjo, Jonas Wulff, Tongzhou Wang, Phillip Isola, Antonio Torralba:
Learning to See by Looking at Noise. NeurIPS 2021: 2556-2569 - 2020
- [c5]Steven Liu, Tongzhou Wang, David Bau, Jun-Yan Zhu, Antonio Torralba:
Diverse Image Generation via Self-Conditioned GANs. CVPR 2020: 14274-14283 - [c4]David Bau, Steven Liu, Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba:
Rewriting a Deep Generative Model. ECCV (1) 2020: 351-369 - [c3]Tongzhou Wang, Phillip Isola:
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere. ICML 2020: 9929-9939 - 2018
- [c2]Tongzhou Wang, Yi Wu, Dave Moore, Stuart J. Russell:
Meta-Learning MCMC Proposals. NeurIPS 2018: 4150-4160 - 2017
- [c1]Pratul P. Srinivasan, Tongzhou Wang, Ashwin Sreelal, Ravi Ramamoorthi, Ren Ng:
Learning to Synthesize a 4D RGBD Light Field from a Single Image. ICCV 2017: 2262-2270
Informal and Other Publications
- 2024
- [i20]Minyoung Huh, Brian Cheung, Tongzhou Wang, Phillip Isola:
The Platonic Representation Hypothesis. CoRR abs/2405.07987 (2024) - [i19]Tongzhou Wang, Yihan Lin:
CycleGAN with Better Cycles. CoRR abs/2408.15374 (2024) - [i18]Adrián Rodríguez-Muñoz, Tongzhou Wang, Antonio Torralba:
Characterizing Model Robustness via Natural Input Gradients. CoRR abs/2409.20139 (2024) - 2023
- [i17]Sangnie Bhardwaj, Willie McClinton, Tongzhou Wang, Guillaume Lajoie, Chen Sun, Phillip Isola, Dilip Krishnan:
Steerable Equivariant Representation Learning. CoRR abs/2302.11349 (2023) - [i16]Rickard Brüel Gabrielsson, Tongzhou Wang, Manel Baradad, Justin Solomon:
Deep Augmentation: Enhancing Self-Supervised Learning through Transformations in Higher Activation Space. CoRR abs/2303.14537 (2023) - [i15]Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang:
Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning. CoRR abs/2304.01203 (2023) - [i14]George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, Jun-Yan Zhu:
Generalizing Dataset Distillation via Deep Generative Prior. CoRR abs/2305.01649 (2023) - 2022
- [i13]George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, Jun-Yan Zhu:
Dataset Distillation by Matching Training Trajectories. CoRR abs/2203.11932 (2022) - [i12]Tongzhou Wang, Simon S. Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian:
Denoised MDPs: Learning World Models Better Than the World Itself. CoRR abs/2206.15477 (2022) - [i11]Tongzhou Wang, Phillip Isola:
On the Learning and Learnablity of Quasimetrics. CoRR abs/2206.15478 (2022) - [i10]Jingwei Ma, Lucy Chai, Minyoung Huh, Tongzhou Wang, Ser-Nam Lim, Phillip Isola, Antonio Torralba:
Totems: Physical Objects for Verifying Visual Integrity. CoRR abs/2209.13032 (2022) - [i9]Tongzhou Wang, Phillip Isola:
Improved Representation of Asymmetrical Distances with Interval Quasimetric Embeddings. CoRR abs/2211.15120 (2022) - [i8]Manel Baradad, Chun-Fu Chen, Jonas Wulff, Tongzhou Wang, Rogério Feris, Antonio Torralba, Phillip Isola:
Procedural Image Programs for Representation Learning. CoRR abs/2211.16412 (2022) - 2021
- [i7]Manel Baradad, Jonas Wulff, Tongzhou Wang, Phillip Isola, Antonio Torralba:
Learning to See by Looking at Noise. CoRR abs/2106.05963 (2021) - 2020
- [i6]Tongzhou Wang, Phillip Isola:
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere. CoRR abs/2005.10242 (2020) - [i5]Steven Liu, Tongzhou Wang, David Bau, Jun-Yan Zhu, Antonio Torralba:
Diverse Image Generation via Self-Conditioned GANs. CoRR abs/2006.10728 (2020) - [i4]David Bau, Steven Liu, Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba:
Rewriting a Deep Generative Model. CoRR abs/2007.15646 (2020) - 2018
- [i3]Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, Alexei A. Efros:
Dataset Distillation. CoRR abs/1811.10959 (2018) - 2017
- [i2]Pratul P. Srinivasan, Tongzhou Wang, Ashwin Sreelal, Ravi Ramamoorthi, Ren Ng:
Learning to Synthesize a 4D RGBD Light Field from a Single Image. CoRR abs/1708.03292 (2017) - [i1]Tongzhou Wang, Yi Wu, David A. Moore, Stuart J. Russell:
Neural Block Sampling. CoRR abs/1708.06040 (2017)
Coauthor Index
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