911 citations · 2.9k across the 99 of their papers we have counts for
14 papers · 1 filter
Dynamic Update-to-Data Ratio: Minimizing World Model Overfitting
Nicolai Dorka, Tim Welschehold, Wolfram Burgard
Early stopping based on the validation set performance is a popular approach to find the right balance between under- and overfitting in the context of supervised learning. However…
Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning
Nicolai Dorka, Tim Welschehold, Joschka Boedecker +1
Accurate value estimates are important for off-policy reinforcement learning. Algorithms based on temporal difference learning typically are prone to an over- or underestimation bi…
Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning
Shengchao Yan, Tim Welschehold, Daniel Büscher +1
The transition from today's mostly human-driven traffic to a purely automated one will be a gradual evolution, with the effect that we will likely experience mixed traffic in the n…
Pre-training of Deep RL Agents for Improved Learning under Domain Randomization
Artemij Amiranashvili, Max Argus, Lukas Hermann +2
Visual domain randomization in simulated environments is a widely used method to transfer policies trained in simulation to real robots. However, domain randomization and augmentat…
Modality-Buffet for Real-Time Object Detection
Nicolai Dorka, Johannes Meyer, Wolfram Burgard
Real-time object detection in videos using lightweight hardware is a crucial component of many robotic tasks. Detectors using different modalities and with varying computational co…
Holistic Filter Pruning for Efficient Deep Neural Networks
Lukas Enderich, Fabian Timm, Wolfram Burgard
Deep neural networks (DNNs) are usually over-parameterized to increase the likelihood of getting adequate initial weights by random initialization. Consequently, trained DNNs have…
Scaling Imitation Learning in Minecraft
Artemij Amiranashvili, Nicolai Dorka, Wolfram Burgard +2
Imitation learning is a powerful family of techniques for learning sensorimotor coordination in immersive environments. We apply imitation learning to attain state-of-the-art perfo…
Efficiency and Equity are Both Essential: A Generalized Traffic Signal Controller with Deep Reinforcement Learning
Shengchao Yan, Jingwei Zhang, Daniel Büscher +1
Traffic signal controllers play an essential role in today's traffic system. However, the majority of them currently is not sufficiently flexible or adaptive to generate optimal tr…
SYMOG: learning symmetric mixture of Gaussian modes for improved fixed-point quantization
Lukas Enderich, Fabian Timm, Wolfram Burgard
Deep neural networks (DNNs) have been proven to outperform classical methods on several machine learning benchmarks. However, they have high computational complexity and require po…
Learning Multimodal Fixed-Point Weights using Gradient Descent
Lukas Enderich, Fabian Timm, Lars Rosenbaum +1
Due to their high computational complexity, deep neural networks are still limited to powerful processing units. To promote a reduced model complexity by dint of low-bit fixed-poin…
Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration
Jingwei Zhang, Niklas Wetzel, Nicolai Dorka +2
Exploration in sparse reward reinforcement learning remains an open challenge. Many state-of-the-art methods use intrinsic motivation to complement the sparse extrinsic reward sign…
Intracranial Error Detection via Deep Learning
Martin Völker, Jiří Hammer, Robin T. Schirrmeister +6
Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalo…
Deep Transfer Learning for Error Decoding from Non-Invasive EEG
Martin Völker, Robin T. Schirrmeister, Lukas D. J. Fiederer +2
We recorded high-density EEG in a flanker task experiment (31 subjects) and an online BCI control paradigm (4 subjects). On these datasets, we evaluated the use of transfer learnin…
Neural SLAM: Learning to Explore with External Memory
Jingwei Zhang, Lei Tai, Ming Liu +2
We present an approach for agents to learn representations of a global map from sensor data, to aid their exploration in new environments. To achieve this, we embed procedures mimi…