  We investigate the nonlinear regression problem under L2 loss (square loss)
functions. Traditional nonlinear regression models often result in non-convex
optimization problems with respect to the parameter set. We show that a convex
nonlinear regression model exists for the traditional least squares problem,
which can be a promising towards designing more complex systems with easier to
train models.
