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Kai Standvoss
Kai Standvoss
Einstein Center for Neurosciences Berlin
Verified email at charite.de
Title
Cited by
Cited by
Year
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
1942017
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
172018
Sensory processing in humans and mice fluctuates between external and internal modes
V Weilnhammer, H Stuke, K Standvoss, P Sterzer
PLoS Biology 21 (12), e3002410, 2023
82023
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
72017
Taking shortcuts: Great for travel, but not for reproducible methods sections
K Standvoss, V Kazezian, BR Lewke, K Bastian, S Chidambaram, S Arafat, ...
bioRxiv, 2022.08. 08.503174, 2022
62022
Humans and mice fluctuate between external and internal modes of sensory processing
V Weilnhammer, H Stuke, AL Eckert, K Standvoss, P Sterzer
bioRxiv, 2021
5*2021
Visual attention through uncertainty minimization in recurrent generative models
K Standvoss, SC Quax, MAJ van Gerven
BioRxiv, 2020.02. 14.948992, 2020
52020
Diffinfinite: Large mask-image synthesis via parallel random patch diffusion in histopathology
M Aversa, G Nobis, M Hägele, K Standvoss, M Chirica, R Murray-Smith, ...
Advances in Neural Information Processing Systems 36, 2024
42024
Discourse Embellishment Using a Deep Encoder-Decoder Network
L Berov, K Standvoss
Computational Creativity and Natural Language Generation, 2018
32018
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
22019
Shortcut citations in the methods section: Frequency, problems, and strategies for responsible reuse
K Standvoss, V Kazezian, BR Lewke, K Bastian, S Chidambaram, S Arafat, ...
Plos Biology 22 (4), e3002562, 2024
2024
AI-driven, mIF-based cell-omics reveals spatially resolved cell signature for outcome prediction in NSCLC patients
S Schallenberg, G Dernbach, S Ruane, C Böhm, L Ruff, K Standvoss, ...
Cancer Research 84 (6_Supplement), 5222-5222, 2024
2024
TTF-1 status in early-stage lung adenocarcinoma is an independent predictor of relapse and survival superior to tumor grading
S Schallenberg, G Dernbach, MP Dragomir, G Schlachtenberger, ...
European Journal of Cancer 197, 113474, 2024
2024
970 Multiplex-immunofluorescence-based spatial characterization of the tumor-microenvironment of a large bicentric clinical non-small cell lung cancer cohort
S Schallenberg, G Dernbach, S Ruane, C Böhm, L Ruff, K Standvoss, ...
Journal for ImmunoTherapy of Cancer 11 (Suppl 1), 2023
2023
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
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
2019
Probabilistic Modeling of Perception and Cognition
S Höffner, L Goerke, A Suckro, V Churavy, K Standvoss, JDF Jäkel
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Articles 1–17