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In this project we have implemented ADMM optimization methods for the convolution layers.
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For the convolution layers, we considered converting a convoluted matrix into a dense toeplitz matrix.
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The weights updates of kernels are done by solving a system of equations by sampling equations obtained from Toeplitz matrix.
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We found that our method worked faster than author's implementation since they only considered the pseudoinverse of the matrix.
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We used MNIST digit dataset (odd-even classification) and found that our method gives 89.6% over traditional backprop method 79.6%.
Code is here.
Report is here.