I am trying to implement https://dl.acm.org/doi/pdf/10.1145/3269206.3271794 .
As it said:
In particular, we integrate the embedding vectors learned from each individual recurrent encoder into a new conclusive embedding vector to jointly consider various time series patterns with different ⟨α, β⟩ configurations
For my understanding, it use multiple individual rnn cell to process different timeseries, then concat all hidden states together to form a 3D input which can use 2d conv extract features .
But I didn't see there is a way to create multiple rnn cells in same layer , do I misunderstand?? If not , could you please give me a guide or an example ?