no code implementations • 14 Feb 2020 • Sammy Christen, Lukas Jendele, Emre Aksan, Otmar Hilliges
We present HiDe, a novel hierarchical reinforcement learning architecture that successfully solves long horizon control tasks and generalizes to unseen test scenarios.
no code implementations • 25 Sep 2019 • Lukas Jendele, Sammy Christen, Emre Aksan, Otmar Hilliges
Hierarchical Reinforcement Learning (HRL) has held the promise to enhance the capabilities of RL agents via operation on different levels of temporal abstraction.
1 code implementation • 20 Feb 2019 • Lukas Jendele, Ondrej Skopek, Anton S. Becker, Ender Konukoglu
Supervised deep learning relies on the assumption that enough training data is available, which presents a problem for its application to several fields, like medical imaging.
2 code implementations • 19 Nov 2018 • Anton S. Becker, Lukas Jendele, Ondrej Skopek, Nicole Berger, Soleen Ghafoor, Magda Marcon, Ender Konukoglu
At the higher resolution, all radiologists showed significantly lower detection rate of cancer in the modified images (0. 77-0. 84 vs. 0. 59-0. 69, p=0. 008), however, they were now able to reliably detect modified images due to better visibility of artifacts (0. 92, 0. 92 and 0. 97).