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I have a general question about supervised ANNs that map inputs to outputs. It is possible to vary the length of the input and output vectors by inserting some dummy variables that will not be considered in the mapping (or will be mapped to other dummy variables). So basically the mapping should look like this (v: value, d:dummy)

Input vector 1 $[v,v,v,v,v] \rightarrow$ Output vector 1 $[v,v,v,v,v]$

Input vector 2 $[v,v,v,v,v]\rightarrow$ Output vector 2 $[v,v,v,v,v]$

Input vector 3 $[v,v,v,d,d] \rightarrow$ Output vector 3 $[v,v,v,d,d]$

Input vector 4 $[v,v,d,d,d] \rightarrow$ Output vector 4 $[v,v,d,d,d]$

Input vector 5 $[v,d,d,d,d] \rightarrow$ Output vector 5 $[v,d,d,d,d]$

The input and output vectors have a length of 5 with 5 values. However, sometimes only a vector of size e.g. 3 (which is basically a vector of length 5 with 2 dummy variables) should be mapped to an output vector of length 3. So after training the ANN should know that if it for example get an input vector of length 3 it should produce an output vector of length 3.

Is something like this generally possible with ANNs or other machine learning approaches? If so, what type of ANN or machine learning approach can be used for this?

Reminder: Can anybody give me more insights into this? I'll appreciate every comment.

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  • $\begingroup$ What is the difference between first two pairs of vectors? $\endgroup$
    – hanugm
    Jul 29 at 10:58
  • $\begingroup$ Thanks hanugm for your answer. Basically there is no difference between the first two pair of vectors. I just wanted to show that we have multiple training data with varying length. The first two pair of vectors turn out to have the same size (same number of v) in this example. $\endgroup$
    – PeterBe
    Jul 29 at 12:17
  • $\begingroup$ Im saying that first I/O vectors and second I/O vectors are same. $\endgroup$
    – hanugm
    Jul 29 at 12:18
  • $\begingroup$ Well this is just an example and the v can stand for any number. The first two pair of vectors can but do not have to be identical (most probably they are not). I just have let's say 1000 input and output vectors and an ANN should be trained to map them correctly. The v stand for any number. So most of the input vectors (even if they have the same size) are not identical. But my question is trageting at having input and output vectors with different lengths $\endgroup$
    – PeterBe
    Jul 29 at 12:22
  • $\begingroup$ Is the output always going to have the same length as the input? In that case you can just simply ignore the part of the output that is past the length of the input. $\endgroup$
    – Taw
    Aug 17 at 19:03

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