Recurrent neural networks (RNNs) have been extraordinarily successful for
prediction with sequential data. To tackle highly variable and noisy real-world
data, we introduce Particle Filter Recurrent Neural Networks (PF-RNNs), a new
RNN family that explicitly models uncertainty in its internal structure: while
an RNN relies on a long, deterministic latent state vector, a PF-RNN maintains
a latent state distribution, approximated as a set of particles. For effective
learning, we provide a fully differentiable particle filter algorithm that
updates the PF-RNN latent state distribution according to the Bayes rule.
Experiments demonstrate that the proposed PF-RNNs outperform the corresponding
standard gated RNNs on a synthetic robot localization dataset and 10 real-world
sequence prediction datasets for text classification, stock price prediction,
etc.

本研究介绍了一种新的递归神经网络的方法，即基于粒子滤波的递归神经网络，它将不确定性建模为一组粒子，并提出了一种全可微分的粒子滤波算法来更新其内部状态分布，实验证明该方法在真实世界序列预测方面优于标准门控递归神经网络。