Why would you do that when it's better to do the opposite? Given a model quantize it and compile it to direct code objects that do the same thing much much much

Why would you do that when it's better to do the opposite? Given a model quantize it and compile it to direct code objects that do the same thing much much much faster? The generality of the approach [NNs] implies that they are effectively a union of all programs that may be represented, and as such there needs to be the capacity for that, this capacity is in size, which makes them wasteful for exact solutions. it is fairly trivial to create FFNNs that behave as decision trees using just relus if you can encode your problem as a continuous problem with a finite set of inputs. Then you can very well say that this decision tree is, well, a program, and there you have it. The actual problem is the encoding, which is why NNs are so powerful, that is, they learn the encodings themselves through grad descent and variants.