%0 Journal Article %1 ghosh2017model %A Ghosh, Soumya %A Doshi-Velez, Finale %D 2017 %K approximate bayesian readings %T Model Selection in Bayesian Neural Networks via Horseshoe Priors %U http://arxiv.org/abs/1705.10388 %X 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. In this work, we apply a horseshoe prior over node pre-activations of a Bayesian neural network, which effectively turns off nodes that do not help explain the data. We demonstrate that our prior prevents the BNN from under-fitting even when the number of nodes required is grossly over-estimated. Moreover, this model selection over the number of nodes doesn't come at the expense of predictive or computational performance; in fact, we learn smaller networks with comparable predictive performance to current approaches.

@article{ghosh2017model, abstract = {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. In this work, we apply a horseshoe prior over node pre-activations of a Bayesian neural network, which effectively turns off nodes that do not help explain the data. We demonstrate that our prior prevents the BNN from under-fitting even when the number of nodes required is grossly over-estimated. Moreover, this model selection over the number of nodes doesn't come at the expense of predictive or computational performance; in fact, we learn smaller networks with comparable predictive performance to current approaches.}, added-at = {2020-01-23T01:41:44.000+0100}, author = {Ghosh, Soumya and Doshi-Velez, Finale}, biburl = {https://www.bibsonomy.org/bibtex/2a24d558a46d6f08307d20ecbbbff8904/kirk86}, description = {[1705.10388] Model Selection in Bayesian Neural Networks via Horseshoe Priors}, interhash = {7c8ac3c6c8f7c04ed380ece5c49ca631}, intrahash = {a24d558a46d6f08307d20ecbbbff8904}, keywords = {approximate bayesian readings}, note = {cite arxiv:1705.10388}, timestamp = {2020-01-23T01:41:44.000+0100}, title = {Model Selection in Bayesian Neural Networks via Horseshoe Priors}, url = {http://arxiv.org/abs/1705.10388}, year = 2017 }