503 citations · 1.2k across the 17 of their papers we have counts for
4 papers · 1 filter
Analyzing biological and artificial neural networks: challenges with opportunities for synergy?
David G. T. Barrett, Ari S. Morcos, Jakob H. Macke
Deep neural networks (DNNs) transform stimuli across multiple processing stages to produce representations that can be used to solve complex tasks, such as object recognition in im…
Measuring abstract reasoning in neural networks
David G. T. Barrett, Felix Hill, Adam Santoro +2
Whether neural networks can learn abstract reasoning or whether they merely rely on superficial statistics is a topic of recent debate. Here, we propose a dataset and challenge des…
Spectral Inference Networks: Unifying Deep and Spectral Learning
David Pfau, Stig Petersen, Ashish Agarwal +2
We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature…
On the importance of single directions for generalization
Ari S. Morcos, David G. T. Barrett, Neil C. Rabinowitz +1
Despite their ability to memorize large datasets, deep neural networks often achieve good generalization performance. However, the differences between the learned solutions of netw…