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Explaining Explanations: An Overview of Interpretability of Machine Learning.

, , , , , and . DSAA, page 80-89. IEEE, (2018)

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Characterizing uncertainty in predictions of genomic sequence-to-activity models., , , , and . MLCB, volume 240 of Proceedings of Machine Learning Research, page 279-297. PMLR, (2023)Student Code Trajectories in an Introductory Programming MOOC., , , and . L@S, page 33:1-33:4. ACM, (2019)Analyzing Student Code Trajectories in an Introductory Programming MOOC., , , and . LWMOOCS, page 53-58. IEEE, (2019)How Student Background and Topic Impact the Doer Effect in Computational Thinking MOOCs., , , , and . LWMOOCS, page 47-52. IEEE, (2019)Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning, , , , , and . (2018)cite arxiv:1806.00069Comment: Edited author email.How to Color a French Flag - Biologically Inspired Algorithms for Scale-Invariant Patterning., , , and . SIROCCO, volume 11639 of Lecture Notes in Computer Science, page 327-331. Springer, (2019)How to Color a French Flag-Biologically Inspired Algorithms for Scale-Invariant Patterning., , , and . CoRR, (2019)How to Color a French Flag - Biologically Inspired Algorithms for Scale-Invariant Patterning., , , and . LATIN, volume 12118 of Lecture Notes in Computer Science, page 413-424. Springer, (2020)Categorizing Resources and Learners for a Finer-Grained Analysis of MOOC Viewing & Doing., , , , and . LWMOOCS, page 116-121. IEEE, (2019)