116 citations · 626 across the 37 of their papers we have counts for
6 papers · 1 filter
Similarity of Pre-trained and Fine-tuned Representations
Thomas Goerttler, Klaus Obermayer
In transfer learning, only the last part of the networks - the so-called head - is often fine-tuned. Representation similarity analysis shows that the most significant change still…
Exploring the Similarity of Representations in Model-Agnostic Meta-Learning
Thomas Goerttler, Klaus Obermayer
In past years model-agnostic meta-learning (MAML) has been one of the most promising approaches in meta-learning. It can be applied to different kinds of problems, e.g., reinforcem…
Training Generative Networks with general Optimal Transport distances
Vaios Laschos, Jan Tinapp, Klaus Obermayer
We propose a new algorithm that uses an auxiliary neural network to express the potential of the optimal transport map between two data distributions. In the sequel, we use the afo…
Regression with Linear Factored Functions
Wendelin Böhmer, Klaus Obermayer
Many applications that use empirically estimated functions face a curse of dimensionality, because the integrals over most function classes must be approximated by sampling. This p…
Risk-sensitive Reinforcement Learning
Yun Shen, Michael J. Tobia, Tobias Sommer +1
We derive a family of risk-sensitive reinforcement learning methods for agents, who face sequential decision-making tasks in uncertain environments. By applying a utility function…
Learning in Riemannian Orbifolds
Brijnesh J. Jain, Klaus Obermayer
Learning in Riemannian orbifolds is motivated by existing machine learning algorithms that directly operate on finite combinatorial structures such as point patterns, trees, and gr…