Towards Understanding the Importance of Noise in Training Neural Networks.
Mo ZhouTianyi LiuYan LiDachao LinEnlu ZhouTuo ZhaoPublished in: CoRR (2019)
Keyphrases
- neural network
- training process
- training algorithm
- feedforward neural networks
- multi layer perceptron
- train a neural network
- feed forward neural networks
- neural network training
- backpropagation algorithm
- training and testing data
- training set
- error back propagation
- back propagation
- additive noise
- noise level
- recurrent networks
- pattern recognition
- multi layer
- noise reduction
- signal to noise ratio
- active learning
- fuzzy logic
- test set
- recurrent neural networks
- genetic algorithm
- artificial neural networks
- noise sensitivity
- deeper understanding
- training examples
- missing data
- neural network model
- data sets
- training patterns
- neural nets
- multilayer neural network
- noisy data
- hidden layer
- input data
- online learning
- fault diagnosis
- random noise
- self organizing maps
- median filter
- relative importance
- rule extraction
- image noise
- noise model
- training phase