Artificial Neural Networks. - Bernhard Mehlig (CoRR, 2019)

Machine learning for active matter. - Frank Cichos; Kristian Gustavsson; Bernhard Mehlig; Giovanni Volpe (Nat. Mach. Intell., 2020)

Datasets for "Collision fluctuations of lucky droplets with superdroplets". - Xiang-Yu Li; Bernhard Mehlig; Gunilla Svensson; Axel Brandenburg; Nils Erland L. Haugen (Zenodo, 2021)

Improving Traffic Sign Recognition by Active Search. - Sami Jaghouar; Hannes Gustafsson; Bernhard Mehlig; Erik Werner; Niklas Gustafsson (GCPR, 2022)

Constraints on parameter choices for successful reservoir computing. - L. Storm; Kristian Gustavsson; Bernhard Mehlig (CoRR, 2022)

Looking at the posterior: on the origin of uncertainty in neural-network classification. - Hampus Linander; Oleksandr Balabanov; Henry Yang; Bernhard Mehlig (CoRR, 2022)

Constraints on parameter choices for successful time-series prediction with echo-state networks. - L. Storm; Kristian Gustavsson; Bernhard Mehlig (Mach. Learn. Sci. Technol., 2022)

Bayesian Posterior Approximation With Stochastic Ensembles. - Oleksandr Balabanov; Bernhard Mehlig; Hampus Linander (CVPR, 2023)

Finite-time Lyapunov exponents of deep neural networks. - L. Storm; Hampus Linander; J. Bec; Kristian Gustavsson; Bernhard Mehlig (CoRR, 2023)

Looking at the posterior: accuracy and uncertainty of neural-network predictions. - Hampus Linander; Oleksandr Balabanov; Henry Yang; Bernhard Mehlig (Mach. Learn. Sci. Technol., 2023)

Vulnerability management digital twin for energy systems. - Jessica B. Heluany; Johannes Götzfried; Bernhard Mehlig; Vasileios Gkioulos (ARES, 2024)