Martin Schrimpf
Martin Schrimpf
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Zitiert von
Zitiert von
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, 2018
The neural architecture of language: Integrative modeling converges on predictive processing
M Schrimpf, IA Blank, G Tuckute, C Kauf, EA Hosseini, NG Kanwisher, ...
Proceedings of the National Academy of Sciences (PNAS) 118 (45), 2021
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 (PNAS) 118 (3), 2021
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 Oral), 12785-12796, 2019
Threedworld: A platform for interactive multi-modal physical simulation
C Gan, J Schwartz, S Alter, M Schrimpf, J Traer, J De Freitas, J Kubilius, ...
Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2021
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
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
Neural Information Processing Systems (NeurIPS Spotlight), 2020
Integrative benchmarking to advance neurally mechanistic models of human intelligence
M Schrimpf, J Kubilius, MJ Lee, NAR Murty, R Ajemian, JJ DiCarlo
Neuron 108 (3), 413-423, 2020
CORnet: Modeling the neural mechanisms of core object recognition
J Kubilius*, M Schrimpf*, A Nayebi, D Bear, DLK Yamins, JJ DiCarlo
bioRxiv, 2018
On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations
N Cheney*, M Schrimpf*, G Kreiman
CBMM Memo, 2017
Is it that simple? Linear mapping models in cognitive neuroscience
AA Ivanova, M Schrimpf, S Anzellotti, N Zaslavsky, E Fedorenko, L Isik
bioRxiv, 438248, 2021
Frivolous Units: Wider Networks Are Not Really That Wide
S Casper, X Boix, V D'Amario, L Guo, M Schrimpf, K Vinken, G Kreiman
Proceedings of the AAAI Conference on Artificial Intelligence, 6921-6929, 2021
Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior
T Marques, M Schrimpf, JJ DiCarlo
bioRxiv, 2021.03. 01.433495, 2021
A Flexible Approach to Automated RNN Architecture Generation
M Schrimpf*, S Merity*, J Bradbury, R Socher
International Conference on Learning Representations (ICLR), 2017
Artificial neural network language models align neurally and behaviorally with humans even after a developmentally realistic amount of training
EA Hosseini, MA Schrimpf, Y Zhang, S Bowman, N Zaslavsky, ...
BioRxiv, 2022.10. 04.510681, 2022
Driving and suppressing the human language network using large language models
G Tuckute, A Sathe, S Srikant, M Taliaferro, M Wang, M Schrimpf, K Kay, ...
BioRxiv, 2023.04. 16.537080, 2023
Aligning model and macaque inferior temporal cortex representations improves model-to-human behavioral alignment and adversarial robustness
J Dapello*, K Kar*, M Schrimpf, R Geary, M Ferguson, DD Cox, J DiCarlo
International Conference on Learning Representations (ICLR Notable Top-5%), 2023
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
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
Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream
F Geiger*, M Schrimpf*, T Marques, J DiCarlo
International Conference on Learning Representations (ICLR Spotlight), 2022
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