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I have 6 sequences, s1,..,s6. Using all sequences I want to predict a binary vector q = [0,0,0,1,1,1,0,0,0,1,1,1,...], which is a mask of the activity of the 6 sequences.

I have looked at seq2seq lstm models, but am struggling with the multiple-sequence-input and single-sequence-output architecture. Am I headed down the right path, or should I shift my focus to a convnet with 6 non-spatial dimensions, and 1 spatial dimension?

Thanks,

Could you please elaborate ? What is a "mask of the activity" ? I don't understand if you have 6 sequences for each datapoint or if you have only 6 datapoints. In short describe your dataset with more accuracy. – Adrien D – 2018-07-16T15:38:24.947

I have N data points, where each data point is a collection of 6 sequences. The length of the sequences varies across different datapoints, but not within data points. So data point n1 has 6 sequences, each of which is length m1, but m2 != m1. @AdrienD Let me know if that makes sense. – Zach LeFevre – 2018-07-17T00:56:36.770