743 citations · 3k across the 44 of their papers we have counts for
6 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…
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning +15
Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based g…
Insights on representational similarity in neural networks with canonical correlation
Ari S. Morcos, Maithra Raghu, Samy Bengio
Comparing different neural network representations and determining how representations evolve over time remain challenging open questions in our understanding of the function of ne…
Pooling is neither necessary nor sufficient for appropriate deformation stability in CNNs
Avraham Ruderman, Neil C. Rabinowitz, Ari S. Morcos +1
Many of our core assumptions about how neural networks operate remain empirically untested. One common assumption is that convolutional neural networks need to be stable to small t…
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…