# Feeding a set of words to neural network

I know that LSTM (or also CNN) is good choice to input a word sequence such as $$(x_1, x_2, ... x_T)$$. But I could not find a good practice to input a set of vectors $$\{x_i\}_{i=1,2,3,...,T}$$, where the order of vector does not have sense.

Let's say, if you have a sentence like "The president gave up to build a wall.'' on document classification, you may feed the sentence to LSTM to know the category of the sentence such as "politics''.

On the other hand, if you have a set of words like {wall, president}, how do you feed these words into a model?

One naive way is calculating embeddings of those words and summing up the embedding vectors to get a feature to feed a model. But, I do not think this feature does not work well.

If you want to learn $$f(X)$$ where $$X$$ is a set, that is the order of elements in the set is inconsequential, then check out the work on Deep Sets [A].