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Found 3 papers, 2 papers with code
Improving the Accuracy and Robustness of CNNs Using a Deep CCA Neural Data Regularizer
As convolutional neural networks (CNNs) become more accurate at object recognition, their representations become more similar to the primate visual system.
Different Spectral Representations in Optimized Artificial Neural Networks and Brains
For convolutional networks, the best $\alpha$ values depend on the task complexity and evaluation metric: lower $\alpha$ values optimized validation accuracy and robustness to adversarial attack for networks performing a simple object recognition task (categorizing MNIST images of handwritten digits); for a more complex task (categorizing CIFAR-10 natural images), we found that lower $\alpha$ values optimized validation accuracy whereas higher $\alpha$ values optimized adversarial robustness.
Analysis of Evolutionary Program Synthesis for Card Games
We report the results by providing a comprehensive analysis of the set of rules and their implications.
