  Mixtures of Experts combine the outputs of several "expert" networks, each of
which specializes in a different part of the input space. This is achieved by
training a "gating" network that maps each input to a distribution over the
experts. Such models show promise for building larger networks that are still
cheap to compute at test time, and more parallelizable at training time. In
this this work, we extend the Mixture of Experts to a stacked model, the Deep
Mixture of Experts, with multiple sets of gating and experts. This
exponentially increases the number of effective experts by associating each
input with a combination of experts at each layer, yet maintains a modest model
size. On a randomly translated version of the MNIST dataset, we find that the
Deep Mixture of Experts automatically learns to develop location-dependent
("where") experts at the first layer, and class-specific ("what") experts at
the second layer. In addition, we see that the different combinations are in
use when the model is applied to a dataset of speech monophones. These
demonstrate effective use of all expert combinations.
