What results do you have on state of the art metrics?
Also, any outline of how one would go about training one of your systems? What kind of dataset does it do best with?
Also, any outline of how one would go about training one of your systems? What kind of dataset does it do best with?
You train these models in the same way you train a neural network, with stochastic gradient descent. For supervised learning, its main benefit is extra flexibility in the speed/precision tradeoff. For time-series problems, we expect this will ultimately allow us to handle data that's collected at irregular intervals - but we haven't yet moved past the prototype stage in that setting.