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Found 7 papers, 0 papers with code
CellMixer: Annotation-free Semantic Cell Segmentation of Heterogeneous Cell Populations
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.
Stable Online and Offline Reinforcement Learning for Antibody CDRH3 Design
The field of antibody-based therapeutics has grown significantly in recent years, with targeted antibodies emerging as a potentially effective approach to personalized therapies.
Multi-intention Inverse Q-learning for Interpretable Behavior Representation
In advancing the understanding of decision-making processes, Inverse Reinforcement Learning (IRL) have proven instrumental in reconstructing animal's multiple intentions amidst complex behaviors.
Robust Tumor Detection from Coarse Annotations via Multi-Magnification Ensembles
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.
NeuRL: Closed-form Inverse Reinforcement Learning for Neural Decoding
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.
Robust and Data-efficient Q-learning by Composite Value-estimation
In the past few years, off-policy reinforcement learning methods have shown promising results in their application for robot control.
Deep Surrogate Q-Learning for Autonomous Driving
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.
