Prediction With Unpredictable Feature Evolution.

Learning with feature evolution studies the scenario where the features of the data streams can evolve, i.e., old features vanish and new features emerge.

Learning With Interpretable Structure From Gated RNN.

The interpretability of deep learning models has raised extended attention these years. It will be beneficial if we can learn an interpretable structure from deep learning models. In this article, we focus on recurrent neural networks (RNNs), especially gated RNNs whose inner mechanism is still not clearly understood.