Training with heavily sparse vectors

I would like train an autoencoder that receives sparse vectors. For example,
a sample of my dataset could be [1 1 1 0 0 0 0 0 …] with more than 8000 zeros. Is there an efficient way to build a mlp model that can make us of high dimensional coordinate lists so I can train it in that way?