In a neural network there are 4 gates: input, output, forget and a gate whose output performs element wise multiplication with the output of the input gate, which is added to the cell state (I don't know the name of this gate, but it's the one in the below picture with the output
Why is the addition of the
C_tilde gate required in the model? In order to allow the input gate to subtract from the cell state, we could change the activation function that results in
tanh and remove the
My reasoning is that the input gate already has a weight matrix
W_i that can is being multiplied to the input gate's input, hence it already does filtering. However, when
C_tilde is multiplied with
i_t that seems to be another unnecessary filter.
My proposed input gate would then be
i_t = tanh(W_i * [h_t-1, x_t] + b_i) and
i_t would directly be added to
C_t = f_t * C_t + i_t rather than
C_t = f_t * C_t + i_t * C_tilde_t).