  Can an $\mathbb{R}^n\rightarrow \mathbb{R}^n$ feedforward network learn
matrix-vector multiplication? This study introduces two mechanisms - flexible
masking to take matrix inputs, and a unique network pruning to respect the
mask's dependency structure. Networks can approximate fixed operations such as
matrix-vector multiplication $\phi(A,x) \rightarrow Ax$, motivating the
mechanisms introduced with applications towards litmus-testing dependencies or
interaction order in graph-based models.
