Understanding projection layer for BLSTM In many research papers there are 'projection layers' related to BLSTM layers. For example, from here:

&quot;we trained an 8-layer BLSTM encoder including 320 cells in each layer
and direction, and the linear projection layer with 320 units followed
by each BLSTM layer&quot;

I can't understand what this means and how it works.
Any help on this topic would be appreciated. Thanks in advance!
A &quot;projection&quot; is a simple linear/dense layer, that is, a matrix multiplication and a bias vector addition.
It is called projection because you &quot;project&quot; a representation of dimensionality $M$ into a representation space of dimensionality $N$.
Sometimes, especially for sequences or 2D data, these projections are implemented as a convolution of size 1, which is equivalent to the computations I described above.