Learning Nonregular Languages: A Comparison of Simple Recurrent Networks and LSTM

Learning Context Sensitive Languages with LSTM Trained with Kalman Filters

Unlike traditional recurrent neural networks, the Long ShortTerm Memory (LSTM) model generalizes well when presented with training sequences derived from regular and also simple nonregular languages. Our novel combination of LSTM and the decoupled extended Kalman filter, however, learns even faster and generalizes even better, requiring only the 10 shortest exemplars (n ≤ 10) of the context sen...

LSTM recurrent networks learn simple context-free and context-sensitive languages

Previous work on learning regular languages from exemplary training sequences showed that long short-term memory (LSTM) outperforms traditional recurrent neural networks (RNNs). We demonstrate LSTMs superior performance on context-free language benchmarks for RNNs, and show that it works even better than previous hardwired or highly specialized architectures. To the best of our knowledge, LSTM ...

Simulate Congestion Prediction in a Wireless Network Using the LSTM Deep Learning Model

Achieved wireless networks since its beginning the prevalent wide due to the increasing wireless devices represented by smart phones and laptop, and the proliferation of networks coincides with the high speed and ease of use of the Internet and enjoy the delivery of various data such as video clips and games. Here's the show the congestion problem arises and represent   aim of the research is t...

Multilingual Recurrent Neural Networks with Residual Learning for Low-Resource Speech Recognition

The shared-hidden-layer multilingual deep neural network (SHL-MDNN), in which the hidden layers of feed-forward deep neural network (DNN) are shared across multiple languages while the softmax layers are language dependent, has been shown to be effective on acoustic modeling of multilingual low-resource speech recognition. In this paper, we propose that the shared-hidden-layer with Long Short-T...

Recurrent Networks That Learn Nonregular Languages