| 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 | 331 | 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 | 90 | 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 | 17 | 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 stochastic gates: Theory and application for linear models S Jana, H Li, Y Yamada, O Lindenbaum arXiv preprint arXiv:2110.15960, 2021 | 8 | 2021 |
| Detection of differentially abundant cell subpopulations discriminates biological states in scRNA-seq data J Zhao, A Jaffe, H Li, O Lindenbaum, E Sefik, R Jackson, X Cheng, ... bioRxiv, 711929, 2019 | 4 | 2019 |
| Autoregressive generative modeling with noise conditional maximum likelihood estimation H Li, Y Kluger arXiv preprint arXiv:2210.10715, 2022 | 3 | 2022 |
| Neural inverse transform sampler H Li, Y Kluger International Conference on Machine Learning, 12813-12825, 2022 | 2 | 2022 |
| 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 | 1 | 2023 |
| Anomaly Detection with Variance Stabilized Density Estimation A Rozner, B Battash, H Li, L Wolf, O Lindenbaum arXiv preprint arXiv:2306.00582, 2023 | 1 | 2023 |
| Variational Random Walk Autoencoders H Li, O Lindenbaum, X Cheng, A Cloninger arXiv preprint arXiv:1905.12724, 2019 | | 2019 |
