Layerwise Interweaving Convolutional LSTM
نویسندگان
چکیده
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 show the model achieves higher accuracy and shoulders lower perplexity on sequential data modeling tasks compared with state of art LSTM models.
منابع مشابه
Forward-Backward Convolutional LSTM for Acoustic Modeling
An automatic speech recognition (ASR) performance has greatly improved with the introduction of convolutional neural network (CNN) or long-short term memory (LSTM) for acoustic modeling. Recently, a convolutional LSTM (CLSTM) has been proposed to directly use convolution operation within the LSTM blocks and combine the advantages of both CNN and LSTM structures into a single architecture. Thi...
متن کاملLong short-term memory based convolutional recurrent neural networks for large vocabulary speech recognition
Long short-term memory (LSTM) recurrent neural networks (RNNs) have been shown to give state-of-the-art performance on many speech recognition tasks, as they are able to provide the learned dynamically changing contextual window of all sequence history. On the other hand, the convolutional neural networks (CNNs) have brought significant improvements to deep feed-forward neural networks (FFNNs),...
متن کاملHigh-Level Music Descriptor Extraction Algorithm Based on Combination of Multi-Channel CNNs and LSTM
Although Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) have yielded impressive performances in a variety of Music Information Retrieval (MIR) tasks, the complementarity among the CNNs of different architectures and that between CNNs and LSTM are seldom considered. In this paper, multichannel CNNs with different architectures and LSTM are combined into one unified archit...
متن کاملYNU-HPCC at SemEval 2017 Task 4: Using A Multi-Channel CNN-LSTM Model for Sentiment Classification
In this paper, we propose a multi-channel convolutional neural network-long shortterm memory (CNN-LSTM) model that consists of two parts: multi-channel CNN and LSTM to analyze the sentiments of short English messages from Twitter. Unlike a conventional CNN, the proposed model applies a multi-channel strategy that uses several filters of different length to extract active local n-gram features i...
متن کاملYNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction
The sentiment analysis in this task aims to indicate the sentiment intensity of the four emotions (e.g. anger, fear, joy, and sadness) expressed in tweets. Compared to the polarity classification, such intensity prediction can provide more finegrained sentiment analysis. In this paper, we present a system that uses a convolutional neural network with long short-term memory (CNN-LSTM) model to c...
متن کامل