266 citations · 505 across the 37 of their papers we have counts for
6 papers · 1 filter
Interpreting Neural Networks through the Polytope Lens
Sid Black, Lee Sharkey, Leo Grinsztajn +8
Mechanistic interpretability aims to explain what a neural network has learned at a nuts-and-bolts level. What are the fundamental primitives of neural network representations? Pre…
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…
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.…