▲ | oli5679 4 days ago | |
https://publications.aston.ac.uk/id/eprint/373/1/NCRG_94_004... mixture density networks are quite interesting if you want probabilistic estimates of neural. here, your model learns to output and array of gaussian distribution coefficient distributions, and mixture weights. these weights are specific to individual observations, and trained to maximise likelihood. | ||
▲ | duvenaud 3 days ago | parent [-] | |
This approach characterizes a different type of uncertainty than BNNs do, and the approaches can be combined. The BNN tracks uncertainty about parameters in the NN, and mixture density nets track the noise distribution _conditional on knowing the parameters_. |