Results 171 to 180 of about 3,974,691 (210)

Application of multi-sensor fusion localization algorithm based on recurrent neural networks. [PDF]

Sci Rep
Huang Z, Ye G, Yang P, Yu W.
europepmc   +1 more source

Recurrent neural networks with transient trajectory explain working memory encoding mechanisms. [PDF]

Commun Biol
Liu C, Jia S, Liu H, Zhao X, Li CT, Xu B, Zhang T, Zhang T.   +7 more
europepmc   +1 more source

Providing Universal Health Care Access to Filipinos Region-wide using Back Propagation and Recurrent Neural Networks for Finding Optimal Locations to Place Rural Health Unit Facilities in the Philippines. [PDF]

Acta Med Philipp
Reyes MTR, Estuar MRJE, Montalan JRE.
europepmc   +1 more source

Bidirectional recurrent neural networks [PDF]

IEEE Transactions on Signal Processing, 1997
In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction.
K K Paliwal
exaly   +2 more sources

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Flexible Recurrent Neural Networks

2021
We introduce two methods enabling recurrent neural networks (RNNs) to trade off accuracy for computational cost during the analysis of a sequence. This opens up the possibility to adapt RNNs in real time to changing computational constraints, such as when running on shared hardware with other processes or in mobile edge computing nodes.
Anne Lambert, Françoise Le Bolzer, François Schnitzler   +2 more
openaire   +1 more source

IDENTIFIABILITY OF RECURRENT NEURAL NETWORKS

Econometric Theory, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Al-Falou, A. A., Trummer, D.
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Pruning in Recurrent Neural Networks

1994
Recurrent neural networks are attracting considerable interest within the neural network domain especially because of their potential in such problems as pattern completion and temporal sequence processing (Almeida, 1987; Hertz et al., 1991). As for feed-forward networks, in virtually all problems of interest the proper number of hidden units is not ...
CASTELLANO G, FANELLI A. M, PELILLO, Marcello   +2 more
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A spiking recurrent neural network

IEEE Computer Society Annual Symposium on VLSI, 2004
A spiking recurrent neural network implementing an associative memory is proposed. The circuit including four integrate-and-fire (IF) and Willshaw-type binary synapses is designed with the AMI 0.5/spl mu/m CMOS process. A large-scale network is simulated with Matlab and its storage capacity is calculated and analyzed.
Yuan Li, John G. Harris
openaire   +2 more sources

Dropout for Recurrent Neural Networks

2019
Neural networks are computational structures which can be trained to perform tasks based on training examples or patterns. Recurrent neural networks are a type of network designed to process time-series data. Dropout is a neural network regularization technique.
Nathan Watt, Mathys C. du Plessis
openaire   +2 more sources