advocates of #machinelearning, I am told that you all know that (current) #ML is limited. fair enough. but which limits are you willing to *publicly* acknowledge?https://twitter.com/NotSimplicio/status/1173373706674085888 …
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If you add memory you get a Turing machine, and then the question is, how do you learn it? I'm a little uncomfortable saying what SGD can and cannot do. It's not a well defined model class anymore.
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I've no clue. But I can say something almost trivial. If you add noise to SGD and scale the learning rate, then it becomes a sampler and sampling can implement anything Bayes. So, algorithmically, we're talking about whether or not the noise in the data is sufficient.
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But then you see something like this: http://www.sontaglab.org/FTPDIR/maass_sontag_analog_neural_networks_gaussian_neural_computation1999.pdf …
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