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Allen Schmaltz
Allen Schmaltz
Verified email at fas.harvard.edu - Homepage
Title
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Cited by
Year
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
2132019
Adapting sequence models for sentence correction
A Schmaltz, Y Kim, AM Rush, SM Shieber
arXiv preprint arXiv:1707.09067, 2017
612017
Word ordering without syntax
A Schmaltz, AM Rush, SM Shieber
arXiv preprint arXiv:1604.08633, 2016
542016
Sentence-level grammatical error identification as sequence-to-sequence correction
A Schmaltz, Y Kim, AM Rush, SM Shieber
arXiv preprint arXiv:1604.04677, 2016
462016
Ecological regression with partial identification
W Jiang, G King, A Schmaltz, MA Tanner
Political Analysis 28 (1), 65-86, 2020
92020
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
92018
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
72021
Exemplar Auditing for Multi-Label Biomedical Text Classification
A Schmaltz, A Beam
arXiv preprint arXiv:2004.03093, 2020
52020
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
12022
Coarse-to-Fine Memory Matching for Joint Retrieval and Classification
A Schmaltz, A Beam
arXiv preprint arXiv:2012.02287, 2020
12020
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
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