English 238 (F 2019) – Bibliography for Class 7

Bibliography for English 238 (F 2019) — Class 7


The following is the part of the English 238 bibliography relevant to this class. (Also see: full course bibliography | bibliographies for topics)

2356229 Class7 1 chicago-fullnote-bibliography 50 creator asc 1 1 1 2855 https://alanyliu.org/wp-content/plugins/zotpress/
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Alvarez-Melis, David, and Tommi Jaakkola. “Towards Robust Interpretability with Self-Explaining Neural Networks.” In Advances in Neural Information Processing Systems 31, edited by S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, 7775–84. Curran Associates, Inc., 2018. http://papers.nips.cc/paper/8003-towards-robust-interpretability-with-self-explaining-neural-networks.pdf. Cite
Blei, David M. “Probabilistic Topic Models.” Commun. ACM 55, no. 4 (2012): 77–84. https://doi.org/10.1145/2133806.2133826. Cite
Guldi, Jo. “Critical Search: A Procedure for Guided Reading in Large-Scale Textual Corpora.” Journal of Cultural Analytics, 2018. https://doi.org/10.22148/16.030. Cite
Hind, Michael, Dennis Wei, Murray Campbell, Noel C. F. Codella, Amit Dhurandhar, Aleksandra Mojsilović, Karthikeyan Natesan Ramamurthy, and Kush R. Varshney. “TED: Teaching AI to Explain Its Decisions.” arXiv:1811.04896 [Cs], 2018. http://arxiv.org/abs/1811.04896. Cite
IBM Research. “AI Explainability 360 - Resources,” 2019. http://aix360.mybluemix.net/resources. Cite
Underwood, Ted. Topic Modeling Made Just Simple Enough., 2012. https://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/. Cite
Wikipedia. “Allegorical Interpretation of the Bible.” In Wikipedia, August 9, 2019. https://en.wikipedia.org/w/index.php?title=Allegorical_interpretation_of_the_Bible&oldid=910120336. Cite

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