Projects
Hessian scientists of various disciplines are using High Performance Computers for their research.
Projects
Kategorie
Jahr
Software
Universität
Cluster
Projekte
Displaying 1 - 14 of 14
Amortized Variational Inference by Policy Search
Variational inference with Gaussian mixture models (GMMs) can be used to learn highly tractable approximations of ...
Engineering SciencesComputer Science
CYWARN – Entwicklung von Strategien und Technologien zur Analyse und Kommunikation der Sicherheitslage im Cyberraum II
The increase in complex cyber-attacks illustrates the vulnerability of society and information infrastructure. In ...
Engineering SciencesComputer Science
Detecting Suitcase Damages With Artificial Intelligence – IT-Project Lufthansa
Artificial intelligence is currently developing faster than ever and introduces many different possibilities. Our client ...
Engineering SciencesComputer Science-
Gradient-Based Variational Inference by Policy Search
Many problems in machine learning involve inference from intractable distributions. For example, when learning latent ...
Engineering SciencesComputer Science 
Imitation Learning for Lap Time Simulation
In order to facilitate rapid prototyping and testing in the advanced motorsport industry, we consider the problem of ...
Engineering SciencesComputer Science
Combining Regenerative and Degenerative Methods in Growing Neural Networks
Neural networks are usually trained with a static architecture. However, the fields of growing and pruning, or ...
Engineering SciencesComputer Science
CYWARN – Entwicklung von Strategien und Technologien zur Analyse und Kommunikation der Sicherheitslage im Cyberraum
The increase in complex cyber-attacks illustrates the vulnerability of society and information infrastructure. In ...
Engineering SciencesComputer Science
High Throughput Optimization of Permanent Magnetic Materials
Magnetic materials are key components for energy harvesting and conversion, information technology and sensor ...
Engineering SciencesMaterials Science-
Noise Resilient Empirical Performance Modeling with Deep Neural Networks
Empirical performance modeling is a proven instrument to analyze the scaling behavior of HPC applications. Using a set ...
Engineering SciencesComputer Science -
Model Learning With Recurrent Kalman Networks
Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system ...
Engineering SciencesComputer Science 
Network Traffic Prediction with Neural Networks
The bursty nature of network traffic is one of the main reasons for congestion in data centers, since traffic loads are ...
Engineering SciencesComputer Science-
Performance Modeling at a Discount
Identifying scalability bugs in parallel applications is a vital but also laborious and expensive task. Empirical ...
Engineering SciencesComputer Science -
Stochastic Search for Applications in Robotics
Stochastic-search algorithms are problem independent algorithms well-suited for black-box optimization of an objective ...
Engineering SciencesComputer Science 
High Throughput Optimization of Permanent Magnetic Materials
Permanent magnets are an important component in a wide range of everyday devices. Present high performance magnets like ...
Natural SciencesTheoretical Condensed Matter Physics
