no code implementations • 1 Dec 2023 • Mehdi Naouar, Gabriel Kalweit, Anusha Klett, Yannick Vogt, Paula Silvestrini, Diana Laura Infante Ramirez, Roland Mertelsmann, Joschka Boedecker, Maria Kalweit
In recent years, several unsupervised cell segmentation methods have been presented, trying to omit the requirement of laborious pixel-level annotations for the training of a cell segmentation model.
no code implementations • 29 Nov 2023 • Yannick Vogt, Mehdi Naouar, Maria Kalweit, Christoph Cornelius Miething, Justus Duyster, Roland Mertelsmann, Gabriel Kalweit, Joschka Boedecker
The field of antibody-based therapeutics has grown significantly in recent years, with targeted antibodies emerging as a potentially effective approach to personalized therapies.
no code implementations • 23 Nov 2023 • Hao Zhu, Brice De La Crompe, Gabriel Kalweit, Artur Schneider, Maria Kalweit, Ilka Diester, Joschka Boedecker
In advancing the understanding of decision-making processes, Inverse Reinforcement Learning (IRL) have proven instrumental in reconstructing animal's multiple intentions amidst complex behaviors.
no code implementations • 29 Mar 2023 • Mehdi Naouar, Gabriel Kalweit, Ignacio Mastroleo, Philipp Poxleitner, Marc Metzger, Joschka Boedecker, Maria Kalweit
In this work, we put the focus back on tumor localization in form of a patch-level classification task and take up the setting of so-called coarse annotations, which provide greater training supervision while remaining feasible from a clinical standpoint.
1 code implementation • 19 Sep 2022 • Erick Rosete-Beas, Oier Mees, Gabriel Kalweit, Joschka Boedecker, Wolfram Burgard
Concretely, we combine a low-level policy that learns latent skills via imitation learning and a high-level policy learned from offline reinforcement learning for skill-chaining the latent behavior priors.
no code implementations • 10 Apr 2022 • Gabriel Kalweit, Maria Kalweit, Mansour Alyahyay, Zoe Jaeckel, Florian Steenbergen, Stefanie Hardung, Thomas Brox, Ilka Diester, Joschka Boedecker
However, since generally there is a strong connection between learning of subjects and their expectations on long-term rewards, we propose NeuRL, an inverse reinforcement learning approach that (1) extracts an intrinsic reward function from collected trajectories of a subject in closed form, (2) maps neural signals to this intrinsic reward to account for long-term dependencies in the behavior and (3) predicts the simulated behavior for unseen neural signals by extracting Q-values and the corresponding Boltzmann policy based on the intrinsic reward values for these unseen neural signals.
1 code implementation • 1 Mar 2022 • Jessica Borja-Diaz, Oier Mees, Gabriel Kalweit, Lukas Hermann, Joschka Boedecker, Wolfram Burgard
Robots operating in human-centered environments should have the ability to understand how objects function: what can be done with each object, where this interaction may occur, and how the object is used to achieve a goal.
no code implementations • 29 Sep 2021 • Gabriel Kalweit, Maria Kalweit, Joschka Boedecker
In the past few years, off-policy reinforcement learning methods have shown promising results in their application for robot control.
no code implementations • 6 Dec 2020 • Branka Mirchevska, Maria Hügle, Gabriel Kalweit, Moritz Werling, Joschka Boedecker
Well-established optimization-based methods can guarantee an optimal trajectory for a short optimization horizon, typically no longer than a few seconds.
no code implementations • 21 Oct 2020 • Maria Kalweit, Gabriel Kalweit, Moritz Werling, Joschka Boedecker
Challenging problems of deep reinforcement learning systems with regard to the application on real systems are their adaptivity to changing environments and their efficiency w. r. t.
no code implementations • 14 Aug 2020 • Maria Hügle, Gabriel Kalweit, Thomas Huegle, Joschka Boedecker
Clinical data from electronic medical records, registries or trials provide a large source of information to apply machine learning methods in order to foster precision medicine, e. g. by finding new disease phenotypes or performing individual disease prediction.
2 code implementations • NeurIPS 2020 • Gabriel Kalweit, Maria Huegle, Moritz Werling, Joschka Boedecker
In this work, we introduce a novel class of algorithms that only needs to solve the MDP underlying the demonstrated behavior once to recover the expert policy.
no code implementations • 20 Mar 2020 • Gabriel Kalweit, Maria Huegle, Moritz Werling, Joschka Boedecker
We analyze the advantages of Constrained Q-learning in the tabular case and compare Constrained DQN to reward shaping and Lagrangian methods in the application of high-level decision making in autonomous driving, considering constraints for safety, keeping right and comfort.
1 code implementation • 21 Oct 2019 • Oier Mees, Markus Merklinger, Gabriel Kalweit, Wolfram Burgard
Our method learns a general skill embedding independently from the task context by using an adversarial loss.
no code implementations • 30 Sep 2019 • Maria Huegle, Gabriel Kalweit, Moritz Werling, Joschka Boedecker
The common pipeline in autonomous driving systems is highly modular and includes a perception component which extracts lists of surrounding objects and passes these lists to a high-level decision component.
no code implementations • 30 Sep 2019 • Gabriel Kalweit, Maria Huegle, Joschka Boedecker
We prove that the combination of these short- and long-term predictions is a representation of the full return, leading to the Composite Q-learning algorithm.
no code implementations • 25 Sep 2019 • Gabriel Kalweit, Maria Huegle, Joschka Boedecker
In the past few years, off-policy reinforcement learning methods have shown promising results in their application for robot control.
no code implementations • 25 Jul 2019 • Maria Huegle, Gabriel Kalweit, Branka Mirchevska, Moritz Werling, Joschka Boedecker
In many real-world decision making problems, reaching an optimal decision requires taking into account a variable number of objects around the agent.