16 citations · 36 across the 10 of their papers we have counts for
11 papers
Few-Shot Learning for Personalised Automated Pain Assessment
Heinke Hihn, Ibrahim Eisawy, Patrick Thiam +2
Pain perception varies substantially across individuals, making it difficult for population-based classifiers to generalise across all subjects in a dataset. One way to account for…
Efficient Architecture Search under Leave-One-Subject-Out Evaluation
Heinke Hihn, Friedhelm Schwenker
Deep neural architectures are widely used for signal processing in automated pain assessment systems. However, architecture design has remained largely a manual task despite the po…
Efficient Leakage-Free Neural Architecture Search under Leave-One-Subject-Out Evaluation
Heinke Hihn
Leave-One-Subject-Out (LOSO) evaluation estimates generalisation performance for subject-based classification but makes Neural Architecture Search (NAS) computationally expensive b…
Ontology-Aligned Embeddings for Data-Driven Labour Market Analytics
Heinke Hihn, Dennis A. V. Dittrich, Carl Jeske +3
The limited ability to reason across occupational data from different sources is a long-standing bottleneck for data-driven labour market analytics. Previous research has relied on…
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…
Mixture-of-Variational-Experts for Continual Learning
Heinke Hihn, Daniel A. Braun
One weakness of machine learning algorithms is the poor ability of models to solve new problems without forgetting previously acquired knowledge. The Continual Learning (CL) paradi…
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…