11 citations · 24 across the 13 of their papers we have counts for
15 papers
Admissable: Training Reinforcement Learning Agents against Adversarial Missingness
Paul Stahlhofen, Luca Hermes, Tim Kochs +2
In order to make Reinforcement Learning algorithms applicable in real world scenarios, safety must be ensured even under adverse operating conditions. In this work, we consider the…
MeGA-MP: Metric Graph Advection Message Passing -- A Physics-Informed Message Passing Operator for Advection-Dominated Metric Graphs
Janine Strotherm, Luca Hermes, André Artelt +1
Many real-world systems are organized as networks where spatio-temporal dynamics unfold along connections and not discretely between nodes. Examples include utility networks such a…
DGPO: RL-Steered Graph Diffusion for Neural Architecture Generation
Aleksei Liuliakov, Luca Hermes, Barbara Hammer
Reinforcement learning fine-tuning has proven effective for steering generative diffusion models toward desired properties in image and molecular domains. Graph diffusion models ha…
Noise Robust One-Class Intrusion Detection on Dynamic Graphs
Aleksei Liuliakov, Alexander Schulz, Luca Hermes +1
In the domain of network intrusion detection, robustness against contaminated and noisy data inputs remains a critical challenge. This study introduces a probabilistic version of t…
One-Class Intrusion Detection with Dynamic Graphs
Aleksei Liuliakov, Alexander Schulz, Luca Hermes +1
With the growing digitalization all over the globe, the relevance of network security becomes increasingly important. Machine learning-based intrusion detection constitutes a promi…
Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks
Maximilian Muschalik, Fabian Fumagalli, Paolo Frazzetto +5
Albeit the ubiquitous use of Graph Neural Networks (GNNs) in machine learning (ML) prediction tasks involving graph-structured data, their interpretability remains challenging. In…
Physics-Informed Graph Neural Networks for Water Distribution Systems
Inaam Ashraf, Janine Strotherm, Luca Hermes +1
Water distribution systems (WDS) are an integral part of critical infrastructure which is pivotal to urban development. As 70% of the world's population will likely live in urban e…
Targeted Visualization of the Backbone of Encoder LLMs
Isaac Roberts, Alexander Schulz, Luca Hermes +1
Attention based Large Language Models (LLMs) are the state-of-the-art in natural language processing (NLP). The two most common architectures are encoders such as BERT, and decoder…
Adversarial Attacks on Leakage Detectors in Water Distribution Networks
Paul Stahlhofen, André Artelt, Luca Hermes +1
Many Machine Learning models are vulnerable to adversarial attacks: There exist methodologies that add a small (imperceptible) perturbation to an input such that the model comes up…
Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities
Moritz Neun, Christian Eichenberger, Yanan Xin +7
Traffic analysis is crucial for urban operations and planning, while the availability of dense urban traffic data beyond loop detectors is still scarce. We present a large-scale fl…
Spatial Graph Convolution Neural Networks for Water Distribution Systems
Inaam Ashraf, Luca Hermes, André Artelt +1
We investigate the task of missing value estimation in graphs as given by water distribution systems (WDS) based on sparse signals as a representative machine learning challenge in…
Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged Agent
W. Zai El Amri, L. Hermes, M. Schilling
Legged locomotion is widespread in nature and has inspired the design of current robots. The controller of these legged robots is often realized as one centralized instance. Howeve…
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
Christian Eichenberger, Moritz Neun, Henry Martin +34
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggrega…
A Graph-based U-Net Model for Predicting Traffic in unseen Cities
Luca Hermes, Barbara Hammer, Andrew Melnik +3
Accurate traffic prediction is a key ingredient to enable traffic management like rerouting cars to reduce road congestion or regulating traffic via dynamic speed limits to maintai…
Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting
Luca Hermes, Barbara Hammer, Malte Schilling
Prediction of movements is essential for successful cooperation with intelligent systems. We propose a model that integrates organized spatial information as given through the movi…