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Johannes Mehrer
Johannes Mehrer
EPFL, CH, previously MRC CBU, Cambridge, UK
Verified email at epfl.ch
Title
Cited by
Cited by
Year
Individual differences among deep neural network models
J Mehrer, CJ Spoerer, TC Kriegeskorte, Nikolaus, Kietzmann
Nature Communications 11, 2020
1582020
An ecologically motivated image dataset for deep learning yields better models of human vision
J Mehrer, CJ Spoerer, EC Jones, N Kriegeskorte, TC Kietzmann
Proceedings of the National Academy of Sciences 118 (8), e2011417118, 2021
142*2021
Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision
CJ Spoerer, TC Kietzmann, J Mehrer, I Charest, N Kriegeskorte
PLOS Computational Biology 16 (10), e1008215, 2020
1142020
Diverse Deep Neural Networks All Predict Human Inferior Temporal Cortex Well, After Training and Fitting
KR Storrs, TC Kietzmann, A Walther, J Mehrer, N Kriegeskorte
Journal of Cognitive Neuroscience 33 (10), 2044-2064, 2021
922021
Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT
A Hernández-García, J Mehrer, N Kriegeskorte, P König, TC Kietzmann
Conference on Cognitive Computational Neuroscience, 2018
112018
Architecture matters: How well neural networks explain it representation does not depend on depth and performance alone
K Storrs, J Mehrer, A Walther, N Kriegeskorte
Conference on Cognitive Computational Neuroscience (CCN), 2017
52017
Computational models of the human visual cortex: on individual differences and ecologically valid input statistics
J Mehrer
University of Cambridge, 2020
2020
Architecture Matters: Training and Structure Both Affect How Well Deep Networks Predict Cortical Representations of Objects, Places and Faces
K Storrs, J Mehrer, A Walther, N Kriegeskorte
PERCEPTION 48, 198-198, 2019
2019
Mokset: A shared stimulus set for ob ect vision research
SR Mok, J Mehrer, N Kriegeskorte
Modelling Human Visual Uncertainty using Bayesian Deep Neural Networks
P McClure, TC Kietzmann, J Mehrer, N Kriegeskorte
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Articles 1–10