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I don't understand why this is downvoted. This is a classic thing to do with deep learning: take something that has a solution that is expensive to compute, and
by carbocation 3y ago
I don't understand why this is downvoted. This is a classic thing to do with deep learning: take something that has a solution that is expensive to compute, and then train a deep learning model from that. And along the way, your model might yield improvements, too, and you can layer in additional features, interpolate at finer-grained resolution, etc. If nothing else, the forward pass in a deep learning model is almost certainly way faster than simulating the next step in a numerical simulation, but there is room for improvement as they show here. Doesn't invalidate the input data!
- danielmarkbruce 3y agoBecause "iterative refinement" is sort of wrong. It's not a refinement and it's not iterative. It's an entirely different model to physical simulation which works entirely differently and the speed up is order of magnitude. Building a statistical model to approximate a physical process isn't a new idea for sure.. there are literally dozens of them for weather.. the idea itself isn't really even iterative, it's the same idea... but it's all in the execution. If you built a model to predict stock prices tomorrow and it generated 1000% pa, it wouldn't be reasonable for me to call it iterative.
- kridsdale3 3y agoIt is iterative when you look at the scope of "humans trying to solve things over time".
- danielmarkbruce 3y agolol, touche.
- andbberger 3y ago"amortized inference" is a better name for it
- borg16 3y ago> the forward pass in a deep learning model is almost certainly way faster than simulating the next step in a numerical simulation Is this the case in most of such refinements (architecture wise)?
- danielmarkbruce 3y agoPractically speaking yes. You'd not likely build a statistical model when you could build a good simulation of the underlying process if the simulation was already really fast and accurate.