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Co-Designing Personal Health? Multidisciplinary Benefits and Challenges in Informing Diabetes Self-Care Technologies., , , , , , , и . Proc. ACM Hum. Comput. Interact., 5 (CSCW2): 457:1-457:26 (2021)What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components., , , и . CoRR, (2022)On the overlooked issue of defining explanation objectives for local-surrogate explainers., , , , и . CoRR, (2021)Scalable and efficient learning from crowds with Gaussian processes., , , , и . Inf. Fusion, (2019)Efficient approximate representations for computationally expensive features., и . ESANN, (2018)Polsar Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field., , и . IGARSS, стр. 708-711. IEEE, (2020)Sampling Based On Natural Image Statistics Improves Local Surrogate Explainers., , , и . BMVC, стр. 1083. BMVA Press, (2022)Understanding Effects of Subjectivity in Measuring Chord Estimation Accuracy., , , и . IEEE ACM Trans. Audio Speech Lang. Process., 21 (12): 2607-2615 (2013)Spectral Clustering and Feature Selection for Microarray Data., и . ICMLA, стр. 425-428. IEEE Computer Society, (2009)Understanding the Properties and Limitations of Contrastive Learning for Out-of-Distribution Detection., , и . ICPR Workshops (1), том 13643 из Lecture Notes in Computer Science, стр. 330-343. Springer, (2022)