56 citations · 167 across the 17 of their papers we have counts for
23 papers
A Tutorial on the Spectral Theory of Markov Chains
Eddie Seabrook, Laurenz Wiskott
Markov chains are a class of probabilistic models that have achieved widespread application in the quantitative sciences. This is in part due to their versatility, but is compounde…
A model of semantic completion in generative episodic memory
Zahra Fayyaz, Aya Altamimi, Sen Cheng +1
Many different studies have suggested that episodic memory is a generative process, but most computational models adopt a storage view. In this work, we propose a computational mod…
Modular Networks Prevent Catastrophic Interference in Model-Based Multi-Task Reinforcement Learning
Robin Schiewer, Laurenz Wiskott
In a multi-task reinforcement learning setting, the learner commonly benefits from training on multiple related tasks by exploiting similarities among them. At the same time, the t…
Reward prediction for representation learning and reward shaping
Hlynur Davíð Hlynsson, Laurenz Wiskott
One of the fundamental challenges in reinforcement learning (RL) is the one of data efficiency: modern algorithms require a very large number of training samples, especially compar…
Singular Sturm-Liouville Problems with Zero Potential (q=0) and Singular Slow Feature Analysis
Stefan Richthofer, Laurenz Wiskott
A Sturm-Liouville problem () is singular if its domain is unbounded or if or vanish at the boundary. Then it is difficult to tell whether profound results fr…
Latent Representation Prediction Networks
Hlynur Davíð Hlynsson, Merlin Schüler, Robin Schiewer +2
Deeply-learned planning methods are often based on learning representations that are optimized for unrelated tasks. For example, they might be trained on reconstructing the environ…