A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
TLDR
The LSTM cell and its variants are reviewed and their variants are explored to explore the learning capacity of the LSTm cell and the L STM networks are divided into two broad categories:LSTM-dominated networks and integrated LSTS networks.Abstract:
Recurrent neural networks (RNNs) have been widely adopted in research areas concerned with sequential data, such as text, audio, and video. However, RNNs consisting of sigma cells or tanh cells are...read more
Citations
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A review on the long short-term memory model
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Deep Learning and Medical Image Processing for Coronavirus (COVID-19) Pandemic: A Survey.
Sweta Bhattacharya,Praveen Kumar Reddy Maddikunta,Quoc-Viet Pham,Thippa Reddy Gadekallu,Siva Rama Krishnan S,Chiranji Lal Chowdhary,Mamoun Alazab,Jalil Piran +7 more
TL;DR: An overview of deep learning and its applications to healthcare found in the last decade is provided and three use cases in China, Korea, and Canada are presented to show deep learning applications for COVID-19 medical image processing.
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EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM
TL;DR: A novel emotion recognition method based on a novel deep learning model (ERDL) which fuses graph convolutional neural network (GCNN) and long-short term memories neural networks (LSTM) and achieves better classification results than state-of-the-art methods.
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Time Series Forecasting of Covid-19 using Deep Learning Models: India-USA Comparative Case Study
TL;DR: Convolution LSTM outperformed the other two models and predicts the Covid-19 cases with high accuracy and very less error for all four datasets of both countries.
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Implicit Deep Learning
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References
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Proceedings ArticleDOI
Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation
Kyunghyun Cho,Bart van Merriënboer,Caglar Gulcehre,Dzmitry Bahdanau,Fethi Bougares,Holger Schwenk,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +8 more
TL;DR: In this paper, the encoder and decoder of the RNN Encoder-Decoder model are jointly trained to maximize the conditional probability of a target sequence given a source sequence.
Proceedings Article
Rectified Linear Units Improve Restricted Boltzmann Machines
Vinod Nair,Geoffrey E. Hinton +1 more
TL;DR: Restricted Boltzmann machines were developed using binary stochastic hidden units that learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset.