Lars Elend and Oliver Kramer. “Self-Organizing Maps with Convolutional Layers”. In: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization - Proceedings of the 13th International Workshop, WSOM+ 2019, Barcelona, Spain, June 26-28, 2019. Ed. by Alfredo Vellido, Karina Gibert, Cecilio Angulo, and José David Martín-Guerrero. Vol. 976. Advances in Intelligent Systems and Computing. Springer, 2019, pp. 23–32. doi: 10.1007/978-3-030-19642-4_3.

Lars Elend, Sebastian A. Tideman, Kerstin Lopatta, and Oliver Kramer. “Earnings Prediction with Deep Learning”. In: KI 2020: Advances in Artificial Intelligence - 43rd German Conference on AI, Bamberg, Germany, September 21-25, 2020, Proceedings. Ed. by Ute Schmid, Franziska Klügl, and Diedrich Wolter. Vol. 12325. Lecture Notes in Computer Science. Springer, 2020, pp. 267–274. doi: 10.1007/978-3-030-58285-2_22.

Tim Cofala, Lars Elend, Philip Mirbach, Jonas Prellberg, Thomas Teusch, and Oliver Kramer. “Evolutionary Multi-objective Design of SARS-CoV-2 Protease Inhibitor Candidates”. In: Parallel Problem Solving from Nature – PPSN XVI. Ed. by Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael Emmerich, and Heike Trautmann. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2020, pp. 357– 371. isbn: 978-3-030-58115-2. doi: 10.1007/978-3-030-58115-2_25.

Lars Elend, Luise Jacobsen, Tim Cofala, Jonas Prellberg, Thomas Teusch, Oliver Kramer, and Ilia A. Solov’yov. “Design of SARS-CoV-2 Main Protease Inhibitors Using Artificial Intelligence and Molecular Dynamic Simulations”. In: Molecules 27.13 (Jan. 2022), p. 4020. issn: 1420-3049. doi: 10.3390/molecules27134020.