Publication

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Filters: Author is Martin Schrimpf  [Clear All Filters]
2019
Kubilius, J. et al. Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 2019-10-28 NeurIPS-camera_ready.pdf (1.88 MB)
2018
Schrimpf, M. & Kubilius, J. Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?. bioRxiv preprint (2018). doi:10.1101/407007PDF icon Brain-Score bioRxiv.pdf (789.83 KB)
Tang, H. et al. Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences (2018). doi:10.1073/pnas.1719397115PDF icon 1719397115.full_.pdf (1.1 MB)
Arend, L. et al. Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results. (2018).PDF icon CBMM-Memo-093.pdf (2.99 MB)
2017
Cheney, N., Schrimpf, M. & Kreiman, G. On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations. (2017).PDF icon CBMM-Memo-065.pdf (687.76 KB)