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After a bit of search I found something that appears to work both well and quickly: http://www.cs.ox.ac.uk/people/yarin.gal/website/blog_3d801aa532c1ce.html htt

After a bit of search I found something that appears to work both well and quickly: http://www.cs.ox.ac.uk/people/yarin.gal/website/blog_3d801aa532c1ce.html http://www.cs.ox.ac.uk/people/yarin.gal/website/blog_3d801aa... The basic idea is brilliant: take a deep neural network with dropout, keep the dropout at evaluation time, run it several times and deduce the uncertainty from the observed variance (with a bit of math). This lets you add an uncertainty estimation to the output of any deep neural network that uses dropout.

lostdog 7y ago
Yes, this is the best I've seen too. Unfortunately, "quickly" is a matter of perspective. Usually you train a neural net that's as large as possible with a runtime that's within your latency and GPU budget. Running the net even twice will blow up your budget. Also, I haven't seen a great evaluation on how well this method works in practice. For example, how well does predicted uncertainty capture mistakes on a real-world dataset?