Sorry for the delay.


Yes, each element in the sequence is a 13-d vector, and each component of each element is a binary (e.g. sequence[0] = [0,0,0,0,0,0,1,1,0,0,0,0,1]).


Actually, the set of possible states of elements in my dataset has is countable (say the number of its elements is 400). So I choose to encode each element into a 400-d one-hot vector, just like the character encoding in RNN.


Like I said above, with method#1, I choose to send the 400-d one-hot vector into RNN and get a 400-d vector (normalized) and view it as the probability distribution. Then I choose the max value of the output vector and decode it to a one-hot vector then to the original 13-d vector. Yes, cross entropy is usually for this method.


Performance means the correct rate of prediction in each step.


It is not a seq2seq model, what I want to do is supply the RNN input with a kind of “sparse representation” of the original 13-d vector. So the encoder and the decoder may be just sparse encoder network.


Hope these answers may be helpful for understanding.