2 code implementations • 23 Jun 2021 • Jens Petersen, Fabian Isensee, Gregor Köhler, Paul F. Jäger, David Zimmerer, Ulf Neuberger, Wolfgang Wick, Jürgen Debus, Sabine Heiland, Martin Bendszus, Philipp Vollmuth, Klaus H. Maier-Hein
The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy.
1 code implementation • 9 Jun 2021 • Jens Petersen, Gregor Köhler, David Zimmerer, Fabian Isensee, Paul F. Jäger, Klaus H. Maier-Hein
Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test time conditioned on a number of context points.
1 code implementation • ICLR 2021 • Jörg K. H. Franke, Gregor Köhler, André Biedenkapp, Frank Hutter
Despite significant progress in challenging problems across various domains, applying state-of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their sensitivity to the choice of hyperparameters.
no code implementations • 28 Oct 2019 • Jörg K. H. Franke, Gregor Köhler, Noor Awad, Frank Hutter
Current Deep Reinforcement Learning algorithms still heavily rely on handcrafted neural network architectures.