Huaipan Jiang

Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training

Recurrent Neural Networks (RNNs), more specifically their Long Short-Term Memory (LSTM) variants, have been widely used as a deep learning tool for tackling sequence-based learning tasks in text and speech. Training of such LSTM applications is computationally intensive due to the recurrent nature o…