16 citations · 16 across the 3 of their papers we have counts for
6 papers
Hierarchically Structured Task-Agnostic Continual Learning
Heinke Hihn, Daniel A. Braun
One notable weakness of current machine learning algorithms is the poor ability of models to solve new problems without forgetting previously acquired knowledge. The Continual Lear…
Binary Classification: Counterbalancing Class Imbalance by Applying Regression Models in Combination with One-Sided Label Shifts
Peter Bellmann, Heinke Hihn, Daniel A. Braun +1
In many real-world pattern recognition scenarios, such as in medical applications, the corresponding classification tasks can be of an imbalanced nature. In the current study, we f…
Specialization in Hierarchical Learning Systems
Heinke Hihn, Daniel A. Braun
Joining multiple decision-makers together is a powerful way to obtain more sophisticated decision-making systems, but requires to address the questions of division of labor and spe…
Hierarchical Expert Networks for Meta-Learning
Heinke Hihn, Daniel A. Braun
The goal of meta-learning is to train a model on a variety of learning tasks, such that it can adapt to new problems within only a few iterations. Here we propose a principled info…
An Information-theoretic On-line Learning Principle for Specialization in Hierarchical Decision-Making Systems
Heinke Hihn, Sebastian Gottwald, Daniel A. Braun
Information-theoretic bounded rationality describes utility-optimizing decision-makers whose limited information-processing capabilities are formalized by information constraints.…
Bounded Rational Decision-Making with Adaptive Neural Network Priors
Heinke Hihn, Sebastian Gottwald, Daniel A. Braun
Bounded rationality investigates utility-optimizing decision-makers with limited information-processing power. In particular, information theoretic bounded rationality models forma…