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[D] Better or worse to include relational data in training/output?

I am solving for a list of 2D features [x,y].

Do you think it’s better to just solve for just the outputs or should I help guide it by reinforcing with relational data. E.g. the vector between 2 points:

param1 [x1,y1],
param2 [x2,y2],
Vector1 [(x1-x2), (y1-y2)],

=> [x1, y1, x2, y2, xVector1, yVector1]
vs
[x1, y1, x2, y2]

My intuition says it’s better to include the ‘hints’ provided by the vectors. But the cautionary voice in my head says “don’t try to impose on the networks, if it’s valuable, it will eventually discover it itself. ” Am I being a naive beginner in trying to help it with additional clues or am I being paranoid in thinking the training will just magically find the optimal solution on its own?

I’ve trained a model on just points and it’s doing okish with my current dataset. And I’m increasing the size of my dataset right now. But I’m also seeing some “obvious” mistakes like points 1 pixel apart from one another. If it was solving for both I feel like the error rate would more accurately reflect whatever is causing the training to not catch those edge cases.

submitted by /u/im_thatoneguy
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.