3 citations · 13 across the 10 of their papers we have counts for
10 papers
Dynamic Robot-Assisted Surgery with Hierarchical Class-Incremental Semantic Segmentation
Julia Hindel, Ema Mekic, Enamundram Naga Karthik +4
Robot-assisted surgeries rely on accurate and real-time scene understanding to safely guide surgical instruments. However, segmentation models trained on static datasets face key l…
BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design
Yannick Vogt, Mehdi Naouar, Maria Kalweit +4
Antibodies offer great potential for the treatment of various diseases. However, the discovery of therapeutic antibodies through traditional wet lab methods is expensive and time-c…
Advances in Land Surface Model-based Forecasting: A comparative study of LSTM, Gradient Boosting, and Feedforward Neural Network Models as prognostic state emulators
Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington +7
Most useful weather prediction for the public is near the surface. The processes that are most relevant for near-surface weather prediction are also those that are most interactive…
Stable Online and Offline Reinforcement Learning for Antibody CDRH3 Design
Yannick Vogt, Mehdi Naouar, Maria Kalweit +5
The field of antibody-based therapeutics has grown significantly in recent years, with targeted antibodies emerging as a potentially effective approach to personalized therapies. S…
CellMixer: Annotation-free Semantic Cell Segmentation of Heterogeneous Cell Populations
Mehdi Naouar, Gabriel Kalweit, Anusha Klett +6
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…
Multi-intention Inverse Q-learning for Interpretable Behavior Representation
Hao Zhu, Brice De La Crompe, Gabriel Kalweit +4
In advancing the understanding of natural decision-making processes, inverse reinforcement learning (IRL) methods have proven instrumental in reconstructing animal's intentions und…
Robust Tumor Detection from Coarse Annotations via Multi-Magnification Ensembles
Mehdi Naouar, Gabriel Kalweit, Ignacio Mastroleo +4
Cancer detection and classification from gigapixel whole slide images of stained tissue specimens has recently experienced enormous progress in computational histopathology. The li…
Process-guidance improves predictive performance of neural networks for carbon turnover in ecosystems
Marieke Wesselkamp, Niklas Moser, Maria Kalweit +2
Despite deep-learning being state-of-the-art for data-driven model predictions, it has not yet found frequent application in ecology. Given the low sample size typical in many envi…
NeuRL: Closed-form Inverse Reinforcement Learning for Neural Decoding
Gabriel Kalweit, Maria Kalweit, Mansour Alyahyay +6
Current neural decoding methods typically aim at explaining behavior based on neural activity via supervised learning. However, since generally there is a strong connection between…
Deep Surrogate Q-Learning for Autonomous Driving
Maria Kalweit, Gabriel Kalweit, Moritz Werling +1
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.…