Results 41 to 50 of about 7,083,147 (335)
Multi-step learning rule for recurrent neural models: an application to time series forecasting [PDF]
, 2001 Multi-step prediction is a difficult task that has attracted increasing interest in recent years. It tries to achieve predictions several steps ahead into the future starting from current information.
Galván, Inés M. +3 more
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Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting [PDF]
IEEE transactions on intelligent transportation systems (Print), 2018 Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks.
Zhiyong Cui +4 more
semanticscholar +1 more source
Recurrent Neural Network Regularization
CoRR, 2014 We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on
Wojciech Zaremba +2 more
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Applications of recurrent neural networks in batch reactors. Part I: NARMA modelling of the dynamic behaviour of the heat transfer fluid [PDF]
, 1997 This paper is focused on the development of nonlinear models, using artificial neural networks, able to provide appropriate predictions when acting as process simulators. The dynamic behaviour of the heat transfer fluid temperature in a jacketed chemical
J.M. Zaldı́var +3 more
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General Recurrent Neural Network for Solving Generalized Linear Matrix Equation
Complexity, 2017 This brief proposes a general framework of the nonlinear recurrent neural network for solving online the generalized linear matrix equation (GLME) with global convergence property. If the linear activation function is utilized, the neural state matrix of
Zhan Li, Hong Cheng, Hongliang Guo
doaj +1 more source
Clustering Based on Continuous Hopfield Network
Mathematics, 2022 Clustering aims to group n data samples into k clusters. In this paper, we reformulate the clustering problem into an integer optimization problem and propose a recurrent neural network with n×k neurons to solve it. We prove the stability and convergence
Yao Xiao +3 more
doaj +1 more source
Bayesian Recurrent Neural Networks
CoRR, 2017 In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by
Meire Fortunato +2 more
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Restricted Recurrent Neural Networks [PDF]
2019 IEEE International Conference on Big Data (Big Data), 2019 Recurrent Neural Network (RNN) and its variations such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), have become standard building blocks for learning online data of sequential nature in many research areas, including natural language processing and speech data analysis.
Enmao Diao, Jie Ding 0002, Vahid Tarokh
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Exponential stability of delayed recurrent neural networks with Markovian jumping parameters [PDF]
, 2006 This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.In this Letter, the global exponential stability analysis problem is considered for a class of recurrent ...
Liu, Y, Yu, L, Wang, Z, Iu, X
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Detection of Shoplifting on Video Using a Hybrid Network
Computation, 2022 Shoplifting is a major problem for shop owners and many other parties, including the police. Video surveillance generates huge amounts of information that staff cannot process in real time.
Lyudmyla Kirichenko +3 more
doaj +1 more source