Comments (5)
Thanks for the question! You can optimize B as usual, e.g., with Adam, since it will get a non-zero gradient in general.
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Isn't it the case that all rows of the B matrix are linearly dependent, making it effectively a rank 1 matrix. Could it be simply reduced to a product of two vectors?
Edit:
Nevermind. I went through the gradient update equations again and it seems like this should not happen as long as other weights of the network are initialized randomly.
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I have the same problem. When I perform gradient backpropagation, the weight of A can be updated, but the weight of B is always 0. Please tell me how should I solve this problem? Thank you!
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I have the same problem. When I perform gradient backpropagation, the weight of A can be updated, but the weight of B is always 0. Please tell me how should I solve this problem? Thank you!
I met the same problem, have u solved it?
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This shouldn't happen. Can you elaborate on your setup?
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