  Using historical data to predict future events has many applications in the
real world, such as stock price prediction; the robot localization. In the past
decades, the Convolutional long short-term memory (LSTM) networks have achieved
extraordinary success with sequential data in the related field. However,
traditional recurrent neural networks (RNNs) keep the hidden states in a
deterministic way. In this paper, we use the particles to approximate the
distribution of the latent state and show how it can extend into a more complex
form, i.e., the Encoder-Decoder mechanism. With the proposed continuous
differentiable scheme, our model is capable of adaptively extracting valuable
information and updating the latent state according to the Bayes rule. Our
empirical studies demonstrate the effectiveness of our method in the prediction
tasks.
