GAN framework to generate synthetic glucose traces.

Some previous works require key point annotations to build reliable correspondence between the source and target meshes, while other methods do not consider any shape correspondence between sources and targets, which leads to limited generation quality.

Forverylargedigraphs, however, this means many (most) entries may be unobserved during training.

In particular, using stochastic gradients in MAML update steps is crucial for RL problems since computation of exact gradients requires access to a large number of possible trajectories.