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Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV), , , , , , и . Proceedings of the 35th International Conference on Machine Learning, том 80 из Proceedings of Machine Learning Research, стр. 2668-2677. PMLR, (2018)From Discovery to Adoption: Understanding the ML Practitioners' Interpretability Journey., , , , , и . Conference on Designing Interactive Systems, стр. 2304-2325. ACM, (2023)The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models., , , , , , , , , и 1 other автор(ы). EMNLP (Demos), стр. 107-118. Association for Computational Linguistics, (2020)ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles., , , , , , , и . IUI, стр. 853-868. ACM, (2024)Approachable Music Composition with Machine Learning at Scale., , , , , , и . ISMIR, стр. 793-800. (2019)Best of both worlds: local and global explanations with human-understandable concepts., , , , , , , , и . CoRR, (2021)Scalable and accurate deep learning for electronic health records., , , , , , , , , и 24 other автор(ы). CoRR, (2018)Scalable and accurate deep learning with electronic health records., , , , , , , , , и 25 other автор(ы). npj Digit. Medicine, (2018)Towards Automatic Concept-based Explanations., , , и . NeurIPS, стр. 9273-9282. (2019)LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models., , , , , , , , , и . CHI Extended Abstracts, стр. 216:1-216:7. ACM, (2024)