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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

Unpublished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.