Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks

6 Feb 2018Patrick SchwabDjordje MiladinovicWalter Karlen

Knowledge of the importance of input features towards decisions made by machine-learning models is essential to increase our understanding of both the models and the underlying data. Here, we present a new approach to estimating feature importance with neural networks based on the idea of distributing the features of interest among experts in an attentive mixture of experts (AME)... (read more)

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