Martin Schrimpf
Title
Cited by
Cited by
Year
Brain-Score: Which artificial neural network for object recognition is most brain-like?
M Schrimpf*, J Kubilius*, H Hong, NJ Majaj, R Rajalingham, EB Issa, ...
BioRxiv, 407007, 2018
1052018
Recurrent computations for visual pattern completion
H Tang*, M Schrimpf*, W Lotter*, C Moerman, A Paredes, JO Caro, ...
Proceedings of the National Academy of Sciences (PNAS) 115 (35), 8835-8840, 2018
932018
CORnet: Modeling the neural mechanisms of core object recognition
J Kubilius*, M Schrimpf*, A Nayebi, D Bear, DLK Yamins, JJ DiCarlo
BioRxiv, 408385, 2018
48*2018
Brain-like object recognition with high-performing shallow recurrent anns
J Kubilius*, M Schrimpf*, K Kar, R Rajalingham, H Hong, N Majaj, E Issa, ...
Advances in Neural Information Processing Systems (NeurIPS), 12785-12796, 2019
352019
On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations
N Cheney*, M Schrimpf*, G Kreiman
CBMM Memo, 2017
202017
A Flexible Approach to Automated RNN Architecture Generation
M Schrimpf*, S Merity*, J Bradbury, R Socher
International Conference on Learning Representations (ICLR), 2017
112017
Should i use tensorflow
M Schrimpf
arXiv preprint arXiv:1611.08903, 2016
82016
Threedworld: A platform for interactive multi-modal physical simulation
C Gan, J Schwartz, S Alter, M Schrimpf, J Traer, J De Freitas, J Kubilius, ...
arXiv preprint arXiv:2007.04954, 2020
72020
Integrative benchmarking to advance neurally mechanistic models of human intelligence
M Schrimpf, J Kubilius, MJ Lee, NAR Murty, R Ajemian, JJ DiCarlo
Neuron, 2020
62020
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations
J Dapello*, T Marques*, M Schrimpf, F Geiger, DD Cox, JJ DiCarlo
BioRxiv, 2020
62020
Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results
L Arend, Y Han, M Schrimpf, P Bashivan, K Kar, T Poggio, JJ DiCarlo, ...
Center for Brains, Minds and Machines (CBMM), 2018
52018
Unsupervised neural network models of the ventral visual stream
C Zhuang, S Yan, A Nayebi, M Schrimpf, MC Frank, JJ DiCarlo, ...
Proceedings of the National Academy of Sciences 118 (3), 2021
42021
Removable and/or Repeated Units Emerge in Overparametrized Deep Neural Networks
S Casper, X Boix, V D'Amario, L Guo, M Schrimpf, K Vinken, G Kreiman
arXiv preprint arXiv:1912.04783, 2019
32019
Continual Learning with Self-Organizing Maps
P Bashivan, M Schrimpf, R Ajemian, I Rish, M Riemer, Y Tu
Neural Information Processing Systems (NeurIPS) Continual Learning Workshop, 2018
32018
Brain-inspired Recurrent Neural Algorithms for Advanced Object Recognition
M Schrimpf
Technical University Munich, LMU Munich, University of Augsburg, 2017
32017
Artificial Neural Networks Accurately Predict Language Processing in the Brain
M Schrimpf, I Blank, G Tuckute, C Kauf, EA Hosseini, N Kanwisher, ...
BioRxiv, 2020
22020
Using brain-score to evaluate and build neural networks for brain-like object recognition
M Schrimpf, J Kubilius, H Hong, NJ Majaj, R Rajalingham, C Ziemba, ...
Cosyne 19, Date: 2019/02/28-2019/03/03, Location: Lisbon, Portugal, 2019
22019
The neural architecture of language: Integrative reverse-engineering converges on a model for predictive processing
M Schrimpf, IA Blank, G Tuckute, C Kauf, EA Hosseini, NG KANWISHER, ...
bioRxiv, 2020
12020
To find better neural network models of human vision, find better neural network models of primate vision
KM Jozwik, M Schrimpf, N Kanwisher, JJ DiCarlo
BioRxiv, 688390, 2019
12019
Is it that simple? The use of linear models in cognitive neuroscience
A Ivanova, M Schrimpf, L Isik, S Anzellotti, N Zaslavsky, E Fedorenko
2020
The system can't perform the operation now. Try again later.
Articles 1–20