We develop instantiations of the PVG for two algorithmic tasks, and show that in practice, the verifier learns a robust decision rule that is able to receive useful and reliable information from an untrusted prover.
Our BCOP parameterization allows us to train large convolutional networks with provable Lipschitz bounds.
In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness.
We identify a necessary property for such an architecture: each of the layers must preserve the gradient norm during backpropagation.
We present a system for training deep neural networks for object detection using synthetic images.