Long short-term memory (LSTM) is normally used in recurrent neural network (RNN) as basic recurrent unit. However, conventional LSTM assumes that the state at current time step depends on previous time step. This assumption constraints the time dependency modeling capability. In this study, we propose a new variation of LSTM, advanced LSTM (A-LSTM), for better temporal context modeling. We empl...

In this paper, we propose a variety of Long Short-Term Memory (LSTM) based models for sequence tagging. These models include LSTM networks, bidirectional LSTM (BI-LSTM) networks, LSTM with a Conditional Random Field (CRF) layer (LSTM-CRF) and bidirectional LSTM with a CRF layer (BI-LSTM-CRF). Our work is the first to apply a bidirectional LSTM CRF (denoted as BI-LSTM-CRF) model to NLP benchmark...

In this paper, a novel architecture for a deep recurrent neural network, residual LSTM is introduced. A plain LSTM has an internal memory cell that can learn long term dependencies of sequential data. It also provides a temporal shortcut path to avoid vanishing or exploding gradients in the temporal domain. The residual LSTM provides an additional spatial shortcut path from lower layers for eff...

A deep network structure is formed with LSTM layer and convolutional layer interweaves with each other. The Layerwise Interweaving Convolutional LSTM(LIC-LSTM) enhanced the feature extraction ability of LSTM stack and is capable for versatile sequential data modeling. Its unique network structure allows it to extract higher level features with sequential information involved. Experiment results...

We introduce a data-driven forecasting method for high dimensional, chaotic systems using Long-Short Term Memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high dimensional dynamical systems in their reduced order space and are shown to be an effective set of non-linear approximators of their attractor. We demonstrate the forecasting performance of ...

In Semantic Role Labeling (SRL) task, the tree structured dependency relation is rich in syntax information, but it is not well handled by existing models. In this paper, we propose Syntax Aware Long Short Time Memory (SA-LSTM). The structure of SA-LSTM changes according to dependency structure of each sentence, so that SA-LSTM can model the whole tree structure of dependency relation in an arc...

Long Short-Term Memory (LSTM) is widely used to solve sequence modeling problems, for example, image captioning. We found the LSTM cells are heavily redundant. We adopt network pruning to reduce the redundancy of LSTM and introduce sparsity as new regularization to reduce overfitting. We can achieve better performance than the dense baseline while reducing the total number of parameters in LSTM...

The long short term memory (LSTM) is a second-order recurrent neural network architecture that excels at storing sequential short-term memories and retrieving them many time-steps later. LSTM's original training algorithm provides the important properties of spatial and temporal locality, which are missing from other training approaches, at the cost of limiting its applicability to a small set ...