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backpropagationSoftware
The internal representations of 'learned' knowledge in neural networks are still poorly understood, even for backpropagation networks. The paper discusses a possible interpretation of learned knowledge of a network trained for parameter estimation from images. The outputs of the hidden layer are the internal components of the output parameters. The input-to-hidden weight maps, functioning as a kind of internal measuring model of the parameter components, include statistical features of the training set and seem to have a clear physical and geometrical meanin
Deep learning, in general, was built on input data transformation and presentation, model training w...
Feedforward neural networks trained by error backpropagation are examples of nonparametric regressio...
International audienceA method for investigating the internal knowledge representation constructed b...
The internal representations of 'learned' knowledge in neural networks are still poorly understood, ...
A large number of experiments have been done on the basic research of parameter estimation from imag...
Despite their success-story, artificial neural networks have one major disadvantage compared to othe...
Despite their success-story, artificial neural networks have one major disadvantagecompared to other...
When we model a higher order functions, such as learning and memory, we face a difficulty of compari...
A common assumption about neural networks is that they can learn an appropriate internal representat...
The recent success of large and deep neural network models has motivated the training of even larger...
Despite their success-story, artificial neural networks have one major disadvantage compared to othe...
In recent years, neural network based image priors have been shown to be highly effective for linear...
Finding useful representations of data in order to facilitate scientific knowledge generation is a u...
Deep learning, in general, was built on input data transformation and presentation, model training w...
There have been a number of recent papers on information theory and neural networks, especially in a...
Deep learning, in general, was built on input data transformation and presentation, model training w...
Feedforward neural networks trained by error backpropagation are examples of nonparametric regressio...
International audienceA method for investigating the internal knowledge representation constructed b...
The internal representations of 'learned' knowledge in neural networks are still poorly understood, ...
A large number of experiments have been done on the basic research of parameter estimation from imag...
Despite their success-story, artificial neural networks have one major disadvantage compared to othe...
Despite their success-story, artificial neural networks have one major disadvantagecompared to other...
When we model a higher order functions, such as learning and memory, we face a difficulty of compari...
A common assumption about neural networks is that they can learn an appropriate internal representat...
The recent success of large and deep neural network models has motivated the training of even larger...
Despite their success-story, artificial neural networks have one major disadvantage compared to othe...
In recent years, neural network based image priors have been shown to be highly effective for linear...
Finding useful representations of data in order to facilitate scientific knowledge generation is a u...
Deep learning, in general, was built on input data transformation and presentation, model training w...
There have been a number of recent papers on information theory and neural networks, especially in a...
Deep learning, in general, was built on input data transformation and presentation, model training w...
Feedforward neural networks trained by error backpropagation are examples of nonparametric regressio...
International audienceA method for investigating the internal knowledge representation constructed b...
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