Evolution of the parameters of the activation functions in the RNN.
- Yuke Li (587532)
- Chunguang Li (61129)
Publication date
May 2013
DOIAbstract
<p>The training data set “MG” is used. (A) The gain parameter . (B) The bias parameter .</p
<p>The training data set “MG” is used. (A) The gain parameter . (B) The bias parameter .</p
An average predictive accuracy graph using training datasets for threshold value identification.</p
<p>GRBM-196-196s were trained on whitened natural image data set with CD-1. The learning curves are ...
<p>The weights of synaptic strengths on the model before (a) and after (b) training trials. (c) The...
<p>The training data set “MG” is used. (A) Mean of the gain parameter of the five hidden neurons. (...
Results from parameter optimisation for HC-RNN with various activation functions and learning rates....
Datasets (systems and benchmarks) used in the selected studies to train RNN models.</p
Results from parameter optimisation for HC-CNN with various activation functions and learning rates....
<p>Subjects were randomly assigned to the training or validation set. All training, including tuning...
Performance of machine learning models on test set using the ROSE-adjusted balanced training set.</p
<p>The training data set “MG” is used. The initial IP learning rates , , , and (no IP) are used for...
<p>The training data set “MG” is used. Neuron 1 (output neuron): (A) Initial input distribution. (B)...
A relationship between the learning rate ? in the learning algorithm, and the slope ß in the nonline...
<p>Training results after 1000-epoch training for the case of the training data set “MG” are present...
(A) Serial position curve exhibits specific characteristics depending on strategy. Left panels shows...
Deep Learning in the field of Big Data has become essential for the analysis and perception of trend...
An average predictive accuracy graph using training datasets for threshold value identification.</p
<p>GRBM-196-196s were trained on whitened natural image data set with CD-1. The learning curves are ...
<p>The weights of synaptic strengths on the model before (a) and after (b) training trials. (c) The...
<p>The training data set “MG” is used. (A) Mean of the gain parameter of the five hidden neurons. (...
Results from parameter optimisation for HC-RNN with various activation functions and learning rates....
Datasets (systems and benchmarks) used in the selected studies to train RNN models.</p
Results from parameter optimisation for HC-CNN with various activation functions and learning rates....
<p>Subjects were randomly assigned to the training or validation set. All training, including tuning...
Performance of machine learning models on test set using the ROSE-adjusted balanced training set.</p
<p>The training data set “MG” is used. The initial IP learning rates , , , and (no IP) are used for...
<p>The training data set “MG” is used. Neuron 1 (output neuron): (A) Initial input distribution. (B)...
A relationship between the learning rate ? in the learning algorithm, and the slope ß in the nonline...
<p>Training results after 1000-epoch training for the case of the training data set “MG” are present...
(A) Serial position curve exhibits specific characteristics depending on strategy. Left panels shows...
Deep Learning in the field of Big Data has become essential for the analysis and perception of trend...
An average predictive accuracy graph using training datasets for threshold value identification.</p
<p>GRBM-196-196s were trained on whitened natural image data set with CD-1. The learning curves are ...
<p>The weights of synaptic strengths on the model before (a) and after (b) training trials. (c) The...
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