Kai Standvoss
Kai Standvoss
Einstein Center for Neurosciences Berlin
Verified email at
Cited by
Cited by
LabVanced: A Unified JavaScript Framework for Online Studies
H Finger, C Goeke, D Diekamp, K Standvoß, P König
International Conference on Computational Social Science, 2017
Cerebral microbleed detection in traumatic brain injury patients using 3D convolutional neural networks
K Standvoss, T Crijns, L Goerke, D Janssen, S Kern, T van Niedek, ...
Medical imaging 2018: computer-aided diagnosis 10575, 314-321, 2018
Encoding And Decoding Dynamic Sensory Signals With Recurrent Neural Networks: An Application Of Conceptors To Birdsongs
R Gast, P Faion, K Standvoss, A Suckro, B Lewis, G Pipa
bioRxiv, 131052, 2017
Visual Attention Through Uncertainty Minimization in Recurrent Generative Models
K Standvoss, SC Quax, MAJ van Gerven
bioRxiv, 2020
Taking shortcuts: Great for travel, but not for reproducible methods sections
K Standvoss, V Kazezian, BR Lewke, K Bastian, S Chidambaram, S Arafat, ...
bioRxiv, 2022
Bimodal inference in humans and mice
V Weilnhammer, H Stuke, K Standvoss, P Sterzer
bioRxiv, 2021.08. 20.457079, 2022
Uncertainty through Sampling: The Correspondence of Monte Carlo Dropout and Spiking in Artificial Neural Networks
K Standvoss, L Grossberger
Conference on Cognitive Computational Neuroscience, 2019
Discourse Embellishment Using a Deep Encoder-Decoder Network
L Berov, K Standvoss
Computational Creativity and Natural Language Generation, 2018
What just happend? Using a volatile Kalman filter to predict perceptual history biases
K Standvoss, M Guggenmos, P Sterzer
PERCEPTION 50 (1_ SUPPL), 12-12, 2021
Task-Dependent Attention Allocation through Uncertainty Minimization in Deep Recurrent Generative Models
K Standvoss, S Quax, M van Gerven
Conference on Cognitive Computational Neuroscience, 2019
Probabilistic Modeling of Perception and Cognition
S Höffner, L Goerke, A Suckro, V Churavy, K Standvoss, JDF Jäkel
The system can't perform the operation now. Try again later.
Articles 1–11