The objective of this ongoing project is the continuous development and advancement of effective simulation methods for ...

Variational inference with Gaussian mixture models (GMMs) can be used to learn highly tractable approximations of ...

The increase in complex cyber-attacks illustrates the vulnerability of society and information infrastructure. In ...

Artificial intelligence is currently developing faster than ever and introduces many different possibilities. Our client ...

Many problems in machine learning involve inference from intractable distributions. For example, when learning latent ...

In order to facilitate rapid prototyping and testing in the advanced motorsport industry, we consider the problem of ...

Recent work has shown that deep neural networks are able to predict human similarity judgments with high accuracy (e.g ...

Neural networks are usually trained with a static architecture. However, the fields of growing and pruning, or ...

The increase in complex cyber-attacks illustrates the vulnerability of society and information infrastructure. In ...

Empirical performance modeling is a proven instrument to analyze the scaling behavior of HPC applications. Using a set ...

Identifying scalability bugs in parallel applications is a vital but also laborious and expensive task. Empirical ...

Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system ...

The bursty nature of network traffic is one of the main reasons for congestion in data centers, since traffic loads are ...

Stochastic-search algorithms are problem independent algorithms well-suited for black-box optimization of an objective ...