[82] LEE, B., SEO, M. K., KIM, D., SHIN, I.-S., SCHICH, M., JEONG, H., AND HAN, S. K. Dissecting
landscape art history with information theory. Proceedings of the National Academy of Sciences 117, 43 (2020),
26580–26590.

[83] LIN, H., VAN ZUIJLEN, M., WIJNTJES, M. W., PONT, S. C., AND BALA, K. Insights from a large-scale

database of material depictions in paintings. arXiv preprint arXiv:2011.12276 (2020).

[84] LLANO, M. T., D’INVERNO, M., YEE-KING, M., MCCORMACK, J., ILSAR, A., PEASE, A., AND COLTON,

S. Explainable computational creativity. In Proc. ICCC (2020).

[85] MADHU, P., KOSTI, R., MÜHRENBERG, L., BELL, P., MAIER, A., AND CHRISTLEIN, V. Recognizing charac-
ters in art history using deep learning. In Proceedings of the 1st Workshop on Structuring and Understanding of
Multimedia heritAge Contents (2019), pp. 15–22.

[86] MADHU, P., MARQUART, T., KOSTI, R., BELL, P., MAIER, A., AND CHRISTLEIN, V. Understanding
In European Conference on
UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK