108 citations · 159 across the 4 of their papers we have counts for
4 papers
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
Jonas Kubilius, Martin Schrimpf, Kohitij Kar +11
Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream. While initially…
Continual Learning with Self-Organizing Maps
Pouya Bashivan, Martin Schrimpf, Robert Ajemian +3
Despite remarkable successes achieved by modern neural networks in a wide range of applications, these networks perform best in domain-specific stationary environments where they a…
A Flexible Approach to Automated RNN Architecture Generation
Martin Schrimpf, Stephen Merity, James Bradbury +1
The process of designing neural architectures requires expert knowledge and extensive trial and error. While automated architecture search may simplify these requirements, the recu…
On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations
Nicholas Cheney, Martin Schrimpf, Gabriel Kreiman
Deep convolutional neural networks are generally regarded as robust function approximators. So far, this intuition is based on perturbations to external stimuli such as the images…