| Clinical concept embeddings learned from massive sources of multimodal medical data AL Beam, B Kompa, A Schmaltz, I Fried, G Weber, N Palmer, X Shi, T Cai, ... PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020, 295-306, 2019 | 230 | 2019 |
| Adapting sequence models for sentence correction A Schmaltz, Y Kim, AM Rush, SM Shieber arXiv preprint arXiv:1707.09067, 2017 | 61 | 2017 |
| Word ordering without syntax A Schmaltz, AM Rush, SM Shieber arXiv preprint arXiv:1604.08633, 2016 | 55 | 2016 |
| Sentence-level grammatical error identification as sequence-to-sequence correction A Schmaltz, Y Kim, AM Rush, SM Shieber arXiv preprint arXiv:1604.04677, 2016 | 48 | 2016 |
| Ecological regression with partial identification W Jiang, G King, A Schmaltz, MA Tanner Political Analysis 28 (1), 65-86, 2020 | 12 | 2020 |
| On the Utility of Lay Summaries and AI Safety Disclosures: Toward Robust, Open Research Oversight A Schmaltz Proceedings of the Second ACL Workshop on Ethics in Natural Language …, 2018 | 9 | 2018 |
| Sharpening the resolution on data matters: a brief roadmap for understanding deep learning for medical data A Schmaltz, AL Beam The Spine Journal 21 (10), 1606-1609, 2021 | 7 | 2021 |
| Exemplar Auditing for Multi-Label Biomedical Text Classification A Schmaltz, A Beam arXiv preprint arXiv:2004.03093, 2020 | 5 | 2020 |
| Detecting Local Insights from Global Labels: Supervised & Zero-Shot Sequence Labeling via a Convolutional Decomposition A Schmaltz arXiv preprint arXiv:1906.01154v3, 2019 | 5* | 2019 |
| Approximate Conditional Coverage via Neural Model Approximations A Schmaltz, D Rasooly arXiv preprint arXiv:2205.14310, 2022 | 1 | 2022 |
| Coarse-to-Fine Memory Matching for Joint Retrieval and Classification A Schmaltz, A Beam arXiv preprint arXiv:2012.02287, 2020 | 1 | 2020 |
| Online Appendix for “Detecting Local Insights from Global Labels: Supervised & Zero-Shot Sequence Labeling via a Convolutional Decomposition” A Schmaltz | | 2019 |
| Learning to Order & Learning to Correct A Schmaltz Harvard University, 2019 | | 2019 |
| Approximate Conditional Coverage & Calibration via Neural Model Approximations A Schmaltz, D Rasooly | | |
| Introspection, Updatability, and Uncertainty Quantification with Transformers: Concrete Methods for AI Safety A Schmaltz, D Rasooly | | |
Stuart ShieberHarvard University在 seas.harvard.edu 的电子邮件经过验证
