Comments (2)
Hi !
I agree and I am confuse for the same reasons. I read the paper and I did not understand how the weight regularizer and dropout regularizer are initialized. Could you please tell us what means prior length scale ? and which value to assign to this variable ?
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I am also confused about this
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Related Issues (13)
- Inconsistency of your code and paper. HOT 3
- dropout_regularizer HOT 2
- p higher than 0.5 after training...
- 'LSTM' object has no attribute 'kernel'
- Contradiction between eqn. (3) and dropout_regularizer? Weight matrix shape vs input shape. HOT 2
- weight_regularizer / dropout_regularizer missing factor 2 HOT 1
- How to adapt heteroscedastic loss for a classification problem? HOT 1
- value to set for weight_regularizer & dropout_regularizer when dataset size is unknown HOT 2
- tensorflow 2 version
- pytorch version: small error?
- Biases in weight_regularizer?
- why upscaling weight by 1/1-p after concrete dropout HOT 1
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