Moreover, from a machine learning perspective, pipeline approaches are problematic because they solve subtasks independently and thus lead to error propagation rather than exploiting interrelationships between variables.
to this, we investigate neural techniques for end-to-end learning in computational AM, which do not require the hand-crafting of features or constraints.