| Understanding Contrastive Representation Learning Through Alignment and Uniformity on the Hypersphere T Wang, P Isola International Conference on Machine Learning (ICML) 2020, 2020 | 1635 | 2020 |
| Dataset Distillation T Wang, JY Zhu, A Torralba, AA Efros arXiv, 2018 | 548 | 2018 |
| Learning to Synthesize a 4D RGBD Light Field From a Single Image PP Srinivasan, T Wang, A Sreelal, R Ramamoorthi, R Ng Proceedings of the IEEE International Conference on Computer Vision (ICCV …, 2017 | 275 | 2017 |
| Dataset Distillation by Matching Training Trajectories G Cazenavette, T Wang, A Torralba, AA Efros, JY Zhu IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022, 2022 | 267 | 2022 |
| Rewriting a Deep Generative Model D Bau, S Liu, T Wang, JY Zhu, A Torralba European Conference on Computer Vision (ECCV) 2020, 351-369, 2020 | 120 | 2020 |
| Diverse Image Generation via Self-Conditioned GANs S Liu, T Wang, D Bau, JY Zhu, A Torralba IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020 …, 2020 | 111 | 2020 |
| Learning to See by Looking at Noise M Baradad Jurjo, J Wulff, T Wang, P Isola, A Torralba Advances in Neural Information Processing Systems (NeurIPS) 2021 34, 2556-2569, 2021 | 88 | 2021 |
| Generalizing Dataset Distillation via Deep Generative Prior G Cazenavette, T Wang, A Torralba, AA Efros, JY Zhu IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2023, 2023 | 46 | 2023 |
| Denoised MDPs: Learning World Models Better Than the World Itself T Wang, SS Du, A Torralba, P Isola, A Zhang, Y Tian International Conference on Machine Learning (ICML) 2022, 2022 | 41 | 2022 |
| Meta-Learning MCMC Proposals T Wang, Y Wu, D Moore, SJ Russell Advances in Neural Information Processing Systems (NeurIPS) 2018 31, 2018 | 38 | 2018 |
| Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning T Wang, A Torralba, P Isola, A Zhang International Conference on Machine Learning (ICML) 2023, 2023 | 19 | 2023 |
| CycleGAN with Better Cycles T Wang, Y Lin Technical Report, 2018 | 14 | 2018 |
| The Platonic Representation Hypothesis M Huh, B Cheung, T Wang, P Isola International Conference on Machine Learning (ICML) 2024, 2024 | 11 | 2024 |
| Procedural Image Programs for Representation Learning M Baradad, CF Chen, J Wulff, T Wang, R Feris, A Torralba, P Isola Advances in Neural Information Processing Systems (NeurIPS) 2022, 2022 | 11 | 2022 |
| Wearable ImageNet: Synthesizing Tileable Textures via Dataset Distillation G Cazenavette, T Wang, A Torralba, AA Efros, JY Zhu IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop …, 2022 | 8 | 2022 |
| On the Learning and Learnability of Quasimetrics T Wang, P Isola International Conference on Learning Representations (ICLR) 2022, 2022 | 8 | 2022 |
| Steerable Equivariant Representation Learning S Bhardwaj, W McClinton, T Wang, G Lajoie, C Sun, P Isola, D Krishnan arXiv, 2023 | 4 | 2023 |
| Improved Representation of Asymmetrical Distances with Interval Quasimetric Embeddings T Wang, P Isola Workshop on Symmetry and Geometry in Neural Representations at NeurIPS 2022, 2022 | 3 | 2022 |
| Totems: Physical Objects for Verifying Visual Integrity J Ma, L Chai, M Huh, T Wang, SN Lim, P Isola, A Torralba European Conference on Computer Vision (ECCV) 2022, 2022 | 3 | 2022 |
| A Framework for Evaluating the Efficacy of Foundation Embedding Models in Healthcare S Xu, H Gui, V Rotemberg, T Wang, Y Chen, R Daneshjou medRxiv, 2024.04. 17.24305983, 2024 | | 2024 |
Phillip IsolaAssociate Professor, MITVerified email at mit.edu
