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# This document contains a list of references to conference proceedings, journal articles, books and websites that are, or have been, relevant to my myself.

Document structure

# A. When possible, documents are referenced in Harvard style
B. Documents are sorted by year of publication in a decreasing order. When a year has multiple items, documents are sorted alphabetically by the last name of the first author.

AI and ML literature

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Unpublished

• Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. To be published by Cambridge University Press. Mathematics for Machine Learning

2019

• Alet, F., Jeewajee, A.K., Villalonga, M.B., Rodriguez, A., Lozano-Perez, T. and Kaelbling, L., 2019, May. Graph Element Networks: adaptive, structured computation and memory. In International Conference on Machine Learning (pp. 212-222).
• Garnelo, Marta, and Murray Shanahan. “Reconciling deep learning with symbolic artificial intelligence: representing objects and relations.” Current Opinion in Behavioral Sciences 29 (2019): 17-23.

2018

• Battaglia, Peter W., et al. “Relational inductive biases, deep learning, and graph networks.” arXiv preprint arXiv:1806.01261 (2018).

2017

• Lin, H.W., Tegmark, M. and Rolnick, D., 2017. Why does deep and cheap learning work so well?. Journal of Statistical Physics, 168(6), pp.1223-1247.

2016

• Garnelo, Marta, Kai Arulkumaran, and Murray Shanahan. “Towards deep symbolic reinforcement learning.” arXiv preprint arXiv:1609.05518 (2016).

2001

• Džeroski, Sašo, Luc De Raedt, and Kurt Driessens. “Relational reinforcement learning.” Machine learning 43.1-2 (2001): 7-52.