7. [M21] In Section 5.6.1 the case of all null weights for neurons with the hyperbolic tangent...

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7. [M21] In Section 5.6.1 the case of all null weights for neurons with the hyperbolic tangent has been discussed. It has been proven that both G1 and G2 are null for OHL networks.

However, the gradient with respect to the bias term was not considered. What is its contribution?

Can we extend the stationarity of the gradient to the case of logistic sigmoid?

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Machine Learning A Constraint-Based Approach

ISBN: 9780081006597

1st Edition

Authors: Marco Gori

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Question Posted: Oct 11, 2024 01:02 PM

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7. [M21] In Section 5.6.1 the case of all null weights for neurons with the hyperbolic tangent has been discussed. It has been proven that both G1 and G2 are null for OHL networks. However, the...
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7. [M21] In Section 5.6.1 the case of all null weights for neurons with the hyperbolic tangent...

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Machine Learning A Constraint-Based Approach

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