The paper derives a posteriori error estimates for neural network depth adaptation by formulating training as an optimal control problem and using dual weighted residuals to insert layers where error is highest.
A genetic programming approach to designing convolutional neural network architectures
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An optimal control approach for neural network architecture adaptation with a posteriori error estimation
The paper derives a posteriori error estimates for neural network depth adaptation by formulating training as an optimal control problem and using dual weighted residuals to insert layers where error is highest.