4 citations · 4 across the 1 of their papers we have counts for
3 papers
Sample-Efficient Automated Deep Reinforcement Learning
Jörg K. H. Franke, Gregor Köhler, André Biedenkapp +1
Despite significant progress in challenging problems across various domains, applying state-of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their…
Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control
Jörg K. H. Franke, Gregor Köhler, Noor Awad +1
Current Deep Reinforcement Learning algorithms still heavily rely on handcrafted neural network architectures. We propose a novel approach to automatically find strong topologies f…
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
Fabian Isensee, Jens Petersen, Andre Klein +8
The U-Net was presented in 2015. With its straight-forward and successful architecture it quickly evolved to a commonly used benchmark in medical image segmentation. The adaptation…