I have $m$ vectors in $\mathbb{R}^n$, where $m >> n$, and I want to train a model to impute a value $x_i$ in $\mathbf{x}$, where $1 \leq i \leq n$ (and can vary by vector).

For instance, I may have the vector $\mathbf{x} = [12.34, 12341, 234, 21.5643, \cdot, 42.2]$, where I want to predict the second-to-last value.

I am guessing in theory this is similar to the masked word problem and approaches similar to BERT may work (though modified for a continuous domain), but I am wondering if their is an existing model architecture that does this?


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