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Scalable and accurate deep learning with electronic health records.

A. Rajkomar, E. Oren, K. Chen, A. Dai, N. Hajaj, M. Hardt, P. Liu, X. Liu, J. Marcus, M. Sun, P. Sundberg, H. Yee, K. Zhang, Y. Zhang, G. Flores, G. Duggan, J. Irvine, Q. Le, K. Litsch, A. Mossin, J. Tansuwan, D. Wang, J. Wexler, J. Wilson, D. Ludwig, S. Volchenboum, K. Chou, M. Pearson, S. Madabushi, N. Shah, A. Butte, M. Howell, C. Cui, G. Corrado, and J. Dean. npj Digit. Medicine, (2018)

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Sergej Wexler

Michael James

Audrein James

Katherine James

Rojin James

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Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)B. Kim, M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, and R. sayres. Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, page 2668-2677. PMLR, (2018)From Discovery to Adoption: Understanding the ML Practitioners' Interpretability Journey.N. Ashtari, R. Mullins, C. Qian, J. Wexler, I. Tenney, and M. Pushkarna. Conference on Designing Interactive Systems, page 2304-2325. ACM, (2023)ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles.S. Petridis, B. Wedin, J. Wexler, M. Pushkarna, A. Donsbach, N. Goyal, C. Cai, and M. Terry. IUI, page 853-868. ACM, (2024)The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models.I. Tenney, J. Wexler, J. Bastings, T. Bolukbasi, A. Coenen, S. Gehrmann, E. Jiang, M. Pushkarna, C. Radebaugh, E. Reif and 1 other author(s). EMNLP (Demos), page 107-118. Association for Computational Linguistics, (2020)Approachable Music Composition with Machine Learning at Scale.C. Huang, C. Hawthorne, A. Roberts, M. Dinculescu, J. Wexler, L. Hong, and J. Howcroft. ISMIR, page 793-800. (2019)Best of both worlds: local and global explanations with human-understandable concepts.J. Schrouff, S. Baur, S. Hou, D. Mincu, E. Loreaux, R. Blanes, J. Wexler, A. Karthikesalingam, and B. Kim. CoRR, (2021)Scalable and accurate deep learning with electronic health records.A. Rajkomar, E. Oren, K. Chen, A. Dai, N. Hajaj, M. Hardt, P. Liu, X. Liu, J. Marcus, M. Sun and 25 other author(s). npj Digit. Medicine, (2018)Scalable and accurate deep learning for electronic health records.A. Rajkomar, E. Oren, K. Chen, A. Dai, N. Hajaj, P. Liu, X. Liu, M. Sun, P. Sundberg, H. Yee and 24 other author(s). CoRR, (2018)Towards Automatic Concept-based Explanations.A. Ghorbani, J. Wexler, J. Zou, and B. Kim. NeurIPS, page 9273-9282. (2019)LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models.M. Kahng, I. Tenney, M. Pushkarna, M. Liu, J. Wexler, E. Reif, K. Kallarackal, M. Chang, M. Terry, and L. Dixon. CHI Extended Abstracts, page 216:1-216:7. ACM, (2024)

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Disambiguation of "Wexler, James"

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Scalable and accurate deep learning with electronic health records.

Please choose a person to relate this publication to

Sergej Wexler

Michael James

Audrein James

Katherine James

Rojin James

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Wexler, James"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Scalable and accurate deep learning with electronic health records.

Please choose a person to relate this publication to

Sergej Wexler

Michael James

Audrein James

Katherine James

Rojin James

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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URL
DOI
BibTeX
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APA
Chicago
DIN 1505
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Scalable and accurate deep learning with electronic health records.