I would like to train a neural network on an input signal and have it learn several unrelated decisions simultaneously (performing binary classification, one-class classification, and linear regression) to tell me different things about the input signal which I care about.
Note that this isn't something like multi-class classification, since I'll be using several different objective functions. Would simply summing my objective functions make the network less likely to converge, or take much longer to train? I will probably have to worry about the relative weights of each objective function. Is there any existing work that shows this is a bad idea? Should I just train several different classifiers independently on the same input signal? My motivation for using a single network is to reduce computation during inference.