| Spectralnet: Spectral clustering using deep neural networks U Shaham, K Stanton, H Li, B Nadler, R Basri, Y Kluger arXiv preprint arXiv:1801.01587, 2018 | 377 | 2018 |
| Detection of differentially abundant cell subpopulations in scRNA-seq data J Zhao, A Jaffe, H Li, O Lindenbaum, E Sefik, R Jackson, X Cheng, ... Proceedings of the National Academy of Sciences 118 (22), e2100293118, 2021 | 130 | 2021 |
| Variational diffusion autoencoders with random walk sampling H Li, O Lindenbaum, X Cheng, A Cloninger Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 18 | 2020 |
| Phase retrieval with holography and untrained priors: Tackling the challenges of low-photon nanoscale imaging H Lawrence, DA Barmherzig, H Li, M Eickenberg, M Gabrié arXiv preprint arXiv:2012.07386, 2020 | 14 | 2020 |
| Support recovery with Projected Stochastic Gates: Theory and application for linear models S Jana, H Li, Y Yamada, O Lindenbaum Signal Processing 213, 109193, 2023 | 12* | 2023 |
| Anomaly detection with variance stabilized density estimation A Rozner, B Battash, H Li, L Wolf, O Lindenbaum arXiv preprint arXiv:2306.00582, 2023 | 3 | 2023 |
| Autoregressive generative modeling with noise conditional maximum likelihood estimation H Li, Y Kluger arXiv preprint arXiv:2210.10715, 2022 | 3 | 2022 |
| Likelihood training of cascaded diffusion models via hierarchical volume-preserving maps H Li, R Basri, Y Kluger arXiv preprint arXiv:2501.06999, 2025 | 2 | 2025 |
| Neural inverse transform sampler H Li, Y Kluger International Conference on Machine Learning, 12813-12825, 2022 | 2 | 2022 |
| Solving Inverse Problems via Diffusion Optimal Control H Li, M Pereira arXiv preprint arXiv:2412.16748, 2024 | | 2024 |
| Boosting Alignment for Post-Unlearning Text-to-Image Generative Models M Ko, H Li, Z Wang, J Patsenker, JT Wang, Q Li, M Jin, D Song, R Jia arXiv preprint arXiv:2412.07808, 2024 | | 2024 |
Yuval KlugerYale Universityyale.edu üzerinde doğrulanmış e-posta adresine sahip
