Martin Schrimpf
Martin Schrimpf
MIT PhD candidate
Bestätigte E-Mail-Adresse bei mit.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
842018
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
83*2018
CORnet: Modeling the neural mechanisms of core object recognition
J Kubilius*, M Schrimpf*, A Nayebi, D Bear, DLK Yamins, JJ DiCarlo
BioRxiv, 408385, 2018
43*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
262019
On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations
N Cheney*, M Schrimpf*, G Kreiman
CBMM Memo, 2017
192017
A Flexible Approach to Automated RNN Architecture Generation
M Schrimpf*, S Merity*, J Bradbury, R Socher
International Conference on Learning Representations (ICLR), 2017
102017
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
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
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
42020
Unsupervised neural network models of the ventral visual stream
C Zhuang, S Yan, A Nayebi, M Schrimpf, M Frank, J DiCarlo, D Yamins
bioRxiv, 2020
32020
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
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
Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence
M Schrimpf, J Kubilius, MJ Lee, NAR Murty, R Ajemian, JJ DiCarlo
Neuron, 2020
12020
Brain-inspired Recurrent Neural Algorithms for Advanced Object Recognition
M Schrimpf
Technical University Munich, LMU Munich, University of Augsburg, 2017
12017
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 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
2020
Artificial Neural Networks Accurately Predict Language Processing in the Brain
M Schrimpf, I Blank, G Tuckute, C Kauf, EA Hosseini, N Kanwisher, ...
BioRxiv, 2020
2020
Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream
F Geiger*, M Schrimpf*, T Marques, J DiCarlo
bioRxiv, 2020
2020
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