Sorry, I’ve made an error. The tying of weights does not refer to simply sharing the embedding layer among encoder and encoder, although this makes sense as well, of course. I’ve looked into that and extended my RNN-based 61 and CNN-based 17 autoencoders to allow for tied weights (just search for self.params.tie_weights in the code)
I don’t think that it’s the perfectly correct implementation, but it works and actually seems to improve the training - I only tested it so far with some smaller dataset.