# Is it possible to decompose a scalar value to a inter-dependent vector neural network?

My data contains a scalar feature $$r$$, I found this feature is important for training my deep model. My idea is supposing there is a 3-layer MLP $$f(x), x \in \mathbb{R}^{n}$$, where $$n=1$$. It outputs a vector with dimension $$m$$ where each value in $$[0, 1]$$.

For my data, it inputs $$r$$ and outputs an m-sized vector.

So here is my decomposition idea make sense?