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This sounds an awful lot like guassian processes which are fairly common in research environments. I don't know how common it is to deploy guassian processes in
by eyegor 5y ago
This sounds an awful lot like guassian processes which are fairly common in research environments. I don't know how common it is to deploy guassian processes in the real world, but I see published papers integrating them into other models all the time. The gist is that instead of input -> prediction you get input -> prediction + sigma (every prediction is given as a guassian distribution).
- andy99 5y agoFor common neural network models, the output probability has no meaning for out of distribution inputs, so you need to do an ensemble or some other method to get at the actual model confidence. I don't know enough about Gaussian processes to know if they have any limitations like that. But it's an interesting point, e.g. if a CNN works better on in-distribution data, but a Gaussian process is better at providing a confidence estimate for OOD points (if it does), a hybrid model is possible.