| COT-GAN: Generating Sequential Data via Causal Optimal Transport T Xu, LK Wenliang, M Munn, B Acciaio Advances in Neural Information Processing Systems 33 pre-proceedings …, 2020 | 94 | 2020 |
| Variational f-divergence minimization M Zhang, T Bird, R Habib, T Xu, D Barber Information Theory workshop, NeurIPS, 2019 | 26 | 2019 |
| Double generative adversarial networks for conditional independence testing C Shi, T Xu, W Bergsma, L Li Journal of Machine Learning Research 22 (285), 1-32, 2021 | 24 | 2021 |
| SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss K Klemmer*, T Xu*, B Acciaio, DB Neill AAAI, 2021 | 16 | 2021 |
| Generative modeling of spatio-temporal weather patterns with extreme event conditioning K Klemmer, S Saha, M Kahl, T Xu, XX Zhu AIMOCC workshop, ICLR, 2021 | 9 | 2021 |
| A residual diffusion model for high perceptual quality codec augmentation NF Ghouse, J Petersen, A Wiggers, T Xu, G Sautiere arXiv preprint arXiv:2301.05489, 2023 | 6 | 2023 |
| Conditional COT-GAN for Video Prediction with Kernel Smoothing T Xu, B Acciaio NeurIPS Workshop on Robustness in Sequence Modeling, 2022 | 6* | 2022 |
| Neural Image Compression with a Diffusion-Based Decoder NF Goose, J Petersen, A Wiggers, T Xu, G Sautière https://arxiv.org/abs/2301.05489, 2023 | 5 | 2023 |
| Generative adversarial networks for sequential learning T Xu London School of Economics and Political Science, 2022 | 1 | 2022 |
| Training generative latent models by variational f-divergence minimization M Zhang, T Bird, R Habib, T Xu, D Barber | 1 | 2018 |
| Diffusion-based data compression NFKM GHOUSE, J Petersen, T Xu, GK SAUTIERE, AJ WIGGERS US Patent App. 18/458,006, 2024 | | 2024 |
| A Residual Diffusion Model for High Perceptual Quality Codec Augmentation N Fathima Ghouse, J Petersen, A Wiggers, T Xu, G Sautière arXiv e-prints, arXiv: 2301.05489, 2023 | | 2023 |
Beatrice AcciaioProfessor, ETH ZurichBestätigte E-Mail-Adresse bei math.ethz.ch
