1 code implementation • 13 Jan 2024 • Michael Kölle, Yannick Erpelding, Fabian Ritz, Thomy Phan, Steffen Illium, Claudia Linnhoff-Popien
Recent advances in Multi-Agent Reinforcement Learning have prompted the modeling of intricate interactions between agents in simulated environments.
no code implementations • 28 Jun 2023 • Michael Kölle, Steffen Illium, Maximilian Zorn, Jonas Nüßlein, Patrick Suchostawski, Claudia Linnhoff-Popien
In the field of wildlife observation and conservation, approaches involving machine learning on audio recordings are becoming increasingly popular.
no code implementations • 18 Jan 2023 • Michael Kölle, Steffen Illium, Carsten Hahn, Lorenz Schauer, Johannes Hutter, Claudia Linnhoff-Popien
The ubiquitous availability of mobile devices capable of location tracking led to a significant rise in the collection of GPS data.
no code implementations • 20 Dec 2022 • Steffen Illium, Thore Schillman, Robert Müller, Thomas Gabor, Claudia Linnhoff-Popien
Common to all different kinds of recurrent neural networks (RNNs) is the intention to model relations between data points through time.
no code implementations • 20 Dec 2022 • Steffen Illium, Maximilian Zorn, Cristian Lenta, Michael Kölle, Claudia Linnhoff-Popien, Thomas Gabor
We introduce organism networks, which function like a single neural network but are composed of several neural particle networks; while each particle network fulfils the role of a single weight application within the organism network, it is also trained to self-replicate its own weights.
no code implementations • 20 Dec 2022 • Steffen Illium, Robert Müller, Andreas Sedlmeier, Claudia-Linnhoff Popien
We apply the vision transformer, a deep machine learning model build around the attention mechanism, on mel-spectrogram representations of raw audio recordings.
no code implementations • 20 Dec 2022 • Steffen Illium, Gretchen Griffin, Michael Kölle, Maximilian Zorn, Jonas Nüßlein, Claudia Linnhoff-Popien
We primarily utilize non-linear recombination of information within an image, fragmenting and occluding small information patches.
1 code implementation • 12 Jun 2022 • Jonas Nüßlein, Steffen Illium, Robert Müller, Thomas Gabor, Claudia Linnhoff-Popien
As a prior, we assume that the higher-level strategy is to reach an unknown target state area, which we hypothesize is a valid prior for many domains in Reinforcement Learning.
no code implementations • 11 Dec 2020 • Robert Müller, Steffen Illium, Fabian Ritz, Kyrill Schmid
In this work, we thoroughly evaluate the efficacy of pretrained neural networks as feature extractors for anomalous sound detection.
no code implementations • 11 Dec 2020 • Robert Müller, Steffen Illium, Fabian Ritz, Tobias Schröder, Christian Platschek, Jörg Ochs, Claudia Linnhoff-Popien
In this work, we present a general procedure for acoustic leak detection in water networks that satisfies multiple real-world constraints such as energy efficiency and ease of deployment.
no code implementations • 11 Aug 2020 • Steffen Illium, Robert Müller, Andreas Sedlmeier, Claudia Linnhoff-Popien
In many fields of research, labeled datasets are hard to acquire.
no code implementations • 5 Jun 2020 • Robert Müller, Fabian Ritz, Steffen Illium, Claudia Linnhoff-Popien
In industrial applications, the early detection of malfunctioning factory machinery is crucial.
no code implementations • 25 May 2020 • Andreas Sedlmeier, Robert Müller, Steffen Illium, Claudia Linnhoff-Popien
One critical prerequisite for the deployment of reinforcement learning systems in the real world is the ability to reliably detect situations on which the agent was not trained.
no code implementations • 11 Apr 2020 • Sebastian Feld, Steffen Illium, Andreas Sedlmeier, Lenz Belzner
In the near future, more and more machines will perform tasks in the vicinity of human spaces or support them directly in their spatially bound activities.
no code implementations • 30 Jul 2019 • Robert Müller, Stefan Langer, Fabian Ritz, Christoph Roch, Steffen Illium, Claudia Linnhoff-Popien
In this work we present STEVE - Soccer TEam VEctors, a principled approach for learning real valued vectors for soccer teams where similar teams are close to each other in the resulting vector space.
no code implementations • 5 Jul 2019 • Daniel Elsner, Stefan Langer, Fabian Ritz, Robert Müller, Steffen Illium
Detecting sleepiness from spoken language is an ambitious task, which is addressed by the Interspeech 2019 Computational Paralinguistics Challenge (ComParE).