| Improving inference for neural image compression Y Yang, R Bamler, S Mandt Advances in Neural Information Processing Systems 33, 573-584, 2020 | 133 | 2020 |
| An introduction to neural data compression Y Yang, S Mandt, L Theis Foundations and Trends® in Computer Graphics and Vision 15 (2), 113-200, 2023 | 121 | 2023 |
| Learning to simulate high energy particle collisions from unlabeled data JN Howard, S Mandt, D Whiteson, Y Yang Scientific Reports 12 (1), 7567, 2022 | 53* | 2022 |
| Hierarchical autoregressive modeling for neural video compression R Yang, Y Yang, J Marino, S Mandt arXiv preprint arXiv:2010.10258, 2020 | 50 | 2020 |
| Variational bayesian quantization Y Yang, R Bamler, S Mandt International Conference on Machine Learning, 10670-10680, 2020 | 33 | 2020 |
| Computationally-Efficient Neural Image Compression with Shallow Decoders Y Yang, S Mandt Proceedings of the IEEE/CVF International Conference on Computer Vision, 530-540, 2023 | 30* | 2023 |
| Towards empirical sandwich bounds on the rate-distortion function Y Yang, S Mandt arXiv preprint arXiv:2111.12166, 2021 | 30 | 2021 |
| Insights from generative modeling for neural video compression R Yang, Y Yang, J Marino, S Mandt IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (8), 9908-9921, 2023 | 14 | 2023 |
| Estimating the rate-distortion function by Wasserstein gradient descent Y Yang, S Eckstein, M Nutz, S Mandt Advances in Neural Information Processing Systems 36, 2024 | 11 | 2024 |
| Autoencoding Implicit Neural Representations for Image Compression T Pham, Y Yang, S Mandt ICML 2023 Workshop Neural Compression: From Information Theory to Applications, 2023 | 5 | 2023 |
| One-shot marginal map inference in Markov random fields H Xiong, Y Guo, Y Yang, N Ruozzi Uncertainty in Artificial Intelligence, 102-112, 2020 | 5 | 2020 |
| Lifted hybrid variational inference Y Chen, Y Yang, S Natarajan, N Ruozzi arXiv preprint arXiv:2001.02773, 2020 | 4 | 2020 |
| Lower Bounding Rate-Distortion From Samples Y Yang, S Mandt Neural Compression: From Information Theory to Applications--Workshop@ ICLR 2021, 2021 | 3 | 2021 |
| Scalable neural network compression and pruning using hard clustering and l1 regularization Y Yang, N Ruozzi, V Gogate arXiv preprint arXiv:1806.05355, 2018 | 3 | 2018 |
| Progressive Compression with Universally Quantized Diffusion Models Y Yang, JC Will, S Mandt arXiv preprint arXiv:2412.10935, 2024 | 1 | 2024 |
| The Ill-defined Problem of Maximum Likelihood Estimation Y Yang | | 2022 |
| Compressing Variational Posteriors Y Yang, R Bamler, S Mandt | | |