Comments (2)
xiamengzhou
commented on August 20, 2024
Given how hidden_z is modeled (with hard concrete distributions), it's essentially learning a discrete binary value of 1 (retain the dimension) or 0 (prune the dimension). As the values strictly fall in side [0, 1], I don't think it will cause gradient explosion issues. For the same reason, it should not have an influence on the magnitude of the last hidden state. Let me know if it is clear!
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sbwww
commented on August 20, 2024
As the values strictly fall in side [0, 1]
Yes, the hidden state won't be too large, but will it be too small? Just plan to check it on the model without re-training.
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