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Matthew Golub

2 accepted papers

2019

Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics

NeurIPS 2019poster

Recurrent neural networks (RNNs) are a widely used tool for modeling sequential data, yet they are often treated as inscrutable black boxes. Given a trained recurrent network, we would like to reverse engineer it--to obtain a quantitative, interpretable description of how it solves a particular task…

Cited by 104SourcePDFScholar
2019

Universality and individuality in neural dynamics across large populations of recurrent networks

NeurIPS 2019spotlight

Many recent studies have employed task-based modeling with recurrent neural networks (RNNs) to infer the computational function of different brain regions. These models are often assessed by quantitatively comparing the low-dimensional neural dynamics of the model and the brain, for example using ca…

Cited by 174SourcePDFScholar