Vanishing gradient problem

In machine learning, the vanishing gradient problem is a difficulty found in training artificial neural networks with gradient-based learning methods… (More)
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Papers overview

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2018
2018
Plain recurrent networks greatly suffer from the vanishing gradient problem while Gated Neural Networks (GNNs) such as Long-short… (More)
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2016
2016
Recursive neural networks (RNN) and their recently proposed extension recursive long short term memory networks (RLSTM) are… (More)
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Highly Cited
2012
Highly Cited
2012
Training Recurrent Neural Networks is more troublesome than feedforward ones because of the vanishing and exploding gradient… (More)
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Highly Cited
2009
Highly Cited
2009
Why are the 2000s so different from the 1 970s? A structural interpretation of changes in the macroeconomic effects of oil prices… (More)
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2007
2007
An important land use change recorded in the Mediterranean basin comprises the abandonment of agricultural lands due to economic… (More)
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2003
2003
Although the methodology for handling ordinal and dichotomous observed variables in structural equation models (SEMs) is… (More)
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Highly Cited
2002
Highly Cited
2002
Does a typical House member need to worry about the electoral ramifications of his roll-call decisions? We investigate the… (More)
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Highly Cited
1999
Highly Cited
1999
We presenta simpleapproach to combiningsceneand auto-calibrationconstraints for thecalibration of cameras… (More)
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Highly Cited
1998
Highly Cited
1998
Received () Revised () Recurrent nets are in principle capable to store past inputs to produce the currently desired output… (More)
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1998
1998
Recurrent nets are in principle capable to store past inputs to produce the currently desired output. This recurrent net property… (More)
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