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A DL model would have to be trained on a dense sampling of all possible kitchens. In the real world it's easy to come up with novel test data points but impossible to have a formal definition for generalization distance, let alone a way to use data aug. to cover the full space.
Naturally you know this and you weren't expecting me to point it out. You frequently send me these bad faith adversarial/ironic replies where you play dumb to make a point. Too used to it to "engage", sorry. It was my mistake for even replying in the first place
No, your mistake was to think that he’s hopelessly missing the point!
I think the solution will rely mostly on: 1) working with programs that are richer than neural networks (i.e. which can express complex and general information processes in a compact form), e.g. going from ML to program synthesis
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