Zobrazeno 1 - 10
of 208
pro vyhledávání: '"Rothkopf, Constantin"'
Bayesian observer and actor models have provided normative explanations for many behavioral phenomena in perception, sensorimotor control, and other areas of cognitive science and neuroscience. They attribute behavioral variability and biases to diff
Externí odkaz:
http://arxiv.org/abs/2409.03710
Autor:
Böhm, Alina, Schneider, Tim, Belousov, Boris, Kshirsagar, Alap, Lin, Lisa, Doerschner, Katja, Drewing, Knut, Rothkopf, Constantin A., Peters, Jan
This paper explores active sensing strategies that employ vision-based tactile sensors for robotic perception and classification of fabric textures. We formalize the active sampling problem in the context of tactile fabric recognition and provide an
Externí odkaz:
http://arxiv.org/abs/2403.13701
Inverse optimal control can be used to characterize behavior in sequential decision-making tasks. Most existing work, however, is limited to fully observable or linear systems, or requires the action signals to be known. Here, we introduce a probabil
Externí odkaz:
http://arxiv.org/abs/2303.16698
Autor:
Hämmerl, Katharina, Deiseroth, Björn, Schramowski, Patrick, Libovický, Jindřich, Rothkopf, Constantin A., Fraser, Alexander, Kersting, Kristian
Pre-trained multilingual language models (PMLMs) are commonly used when dealing with data from multiple languages and cross-lingual transfer. However, PMLMs are trained on varying amounts of data for each language. In practice this means their perfor
Externí odkaz:
http://arxiv.org/abs/2211.07733
Commonly in reinforcement learning (RL), rewards are discounted over time using an exponential function to model time preference, thereby bounding the expected long-term reward. In contrast, in economics and psychology, it has been shown that humans
Externí odkaz:
http://arxiv.org/abs/2209.13413
The human prioritization of image regions can be modeled in a time invariant fashion with saliency maps or sequentially with scanpath models. However, while both types of models have steadily improved on several benchmarks and datasets, there is stil
Externí odkaz:
http://arxiv.org/abs/2207.04250
Bayesian models of behavior have provided computational level explanations in a range of psychophysical tasks. One fundamental experimental paradigm is the production or reproduction task, in which subjects are instructed to generate an action that e
Externí odkaz:
http://arxiv.org/abs/2112.15521
Computational level explanations based on optimal feedback control with signal-dependent noise have been able to account for a vast array of phenomena in human sensorimotor behavior. However, commonly a cost function needs to be assumed for a task an
Externí odkaz:
http://arxiv.org/abs/2110.11130
In explanatory interactive learning (XIL) the user queries the learner, then the learner explains its answer to the user and finally the loop repeats. XIL is attractive for two reasons, (1) the learner becomes better and (2) the user's trust increase
Externí odkaz:
http://arxiv.org/abs/2110.02395
Combining the outputs of multiple classifiers or experts into a single probabilistic classification is a fundamental task in machine learning with broad applications from classifier fusion to expert opinion pooling. Here we present a hierarchical Bay
Externí odkaz:
http://arxiv.org/abs/2106.01770