S. Reipert et al., “Agitation modules: Flexible means to accelerate automated freeze substitution,” Journal of Histochemistry and Cytochemistry, vol. 66, no. 12. SAGE Publications, pp. 903–921, 2018.
LibreCat: https://research-explorer.ista.ac.at/record/163
DOI: 10.1369/0022155418786698
WoS: 000452277700005
PMID: 29969056

S. Sahoo, C. Lampert, and G. S. Martius, “Learning equations for extrapolation and control,” in Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, 2018, vol. 80, pp. 4442–4450.
LibreCat: https://research-explorer.ista.ac.at/record/6012
WoS: 000683379204058
arXiv: 1806.07259

I. Kuzborskij and C. Lampert, “Data-dependent stability of stochastic gradient descent,” in Proceedings of the 35 th International Conference on Machine Learning, Stockholm, Sweden, 2018, vol. 80, pp. 2815–2824.
LibreCat: https://research-explorer.ista.ac.at/record/6011
WoS: 000683379202095
arXiv: 1703.01678

P. Danowski, An Austrian proposal for the Classification of Open Access Tuples (COAT) - Distinguish different Open Access types beyond colors. 2018.
LibreCat: https://research-explorer.ista.ac.at/record/5686
DOI: 10.5281/zenodo.1244154

D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” in Advances in Neural Information Processing Systems 31, Montreal, Canada, 2018, vol. Volume 2018, pp. 5973–5983.
LibreCat: https://research-explorer.ista.ac.at/record/6589
WoS: 000461852000047
arXiv: 1809.10505