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Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors
Soumya Ghosh · Jiayu Yao · Finale Doshi-Velez
Bayesian Neural Networks (BNNs) have recently received increasing attention for their ability to provide well-calibrated posterior uncertainties. However, model selection---even choosing the number of nodes---remains an open question. Recent work has proposed the use of a horseshoe prior over node pre-activations of a Bayesian neural network, which effectively turns off nodes that do not help explain the data. In this work, we propose several modeling and inference advances that consistently improve the compactness of the model learned while maintaining predictive performance, especially in smaller-sample settings including reinforcement learning.
Author Information
Soumya Ghosh (IBM Research)
Jiayu Yao (Harvard University)
Finale Doshi-Velez (Harvard University)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Poster: Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors »
Thu. Jul 12th 04:15 -- 07:00 PM Room Hall B #193
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