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.

本文扩展了专家集成模型，提出了深度专家集成模型，该模型利用多组门和专家实现了输入空间的多重化，同时保持了小型化规模，在 MNIST 数据集和语音单音素数据集上的实验结果表明，该模型能够自动发现局部化的位置相关专家和类别相关专家，并有效地使用所有专家组合。

深层专家混合模型中的因子表示学习