I have a regression problem where I'd like to train an LSTM to tackle it. However, since I do not have too many samples for training (only 2000), I am thinking about using only one hidden node, since the representation of the input is a 300-d vector.

Does it make sense to use only one hidden node (I have never seen such architecture in papers)? Or should I just use something else (e.g. CRF) given the small amount of training samples that I have?

  • $\begingroup$ It doesn't make much sense to me to have an LSTM with just one hidden dimension. $\endgroup$ – Aaron May 14 '18 at 16:18

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