| Defending against adversarial images using basis functions transformations U Shaham, J Garritano, Y Yamada, E Weinberger, A Cloninger, X Cheng, ... arXiv preprint arXiv:1803.10840, 2018 | 69 | 2018 |
| Learning deep attribution priors based on prior knowledge E Weinberger, J Janizek, SI Lee Advances in Neural Information Processing Systems 33, 14034-14045, 2020 | 24 | 2020 |
| Isolating salient variations of interest in single-cell data with contrastiveVI E Weinberger, C Lin, SI Lee Nature Methods 20 (9), 1336-1345, 2023 | 16 | 2023 |
| Histopathologic and machine deep learning criteria to predict lymphoma transformation in bone marrow biopsies L Irshaid, J Bleiberg, E Weinberger, J Garritano, RM Shallis, J Patsenker, ... Archives of Pathology & Laboratory Medicine 146 (2), 182-193, 2022 | 15 | 2022 |
| Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer’s disease neuropathologies N Beebe-Wang, S Celik, E Weinberger, P Sturmfels, PL De Jager, ... Nature Communications 12 (1), 5369, 2021 | 14 | 2021 |
| Moment matching deep contrastive latent variable models E Weinberger, N Beebe-Wang, SI Lee arXiv preprint arXiv:2202.10560, 2022 | 13 | 2022 |
| Disentangling shared and group-specific variations in single-cell transcriptomics data with multiGroupVI E Weinberger, R Lopez, JC Hütter, A Regev Machine Learning in Computational Biology, 16-32, 2022 | 9 | 2022 |
| Feature Selection in the Contrastive Analysis Setting E Weinberger, I Covert, SI Lee Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| A deep generative model of single-cell methylomic data E Weinberger, SI Lee NeurIPS 2023 Generative AI and Biology (GenBio) Workshop, 2023 | | 2023 |
| Isolating structured salient variations in single-cell transcriptomic data with StrastiveVI W Qiu, E Weinberger, SI Lee bioRxiv, 2023.10. 06.561320, 2023 | | 2023 |
| Transferable representations of single-cell transcriptomic data E Weinberger, SI Lee bioRxiv, 2021.04. 13.439707, 2021 | | 2021 |
| Towards scalable embedding models for spatial transcriptomics data SY Moon, E Weinberger, SI Lee | | |
| Isolating salient variations of interest in single-cell E Weinberger, C Lin, SI Lee | | |
| Deep-learning-based isolation of perturbation-induced E Weinberger, SI Lee | | |
