The embedding of such a manifold into the high-dimensional space of the data is achieved via learnable invertible transformations.

Neural Information Processing Systems http://nips.cc/

A counter-intuitive observation is that a more expansive teacher does not make a better student, but the reasons for this phenomenon remain unclear.

Object, attribute or stylistic changes can be learned from visually static datasets. On the other hand, high-quality data for action and reasoning-centric edits is scarce and has to come from entirely different sources that cover e.g.

Here, we propose a hybrid model, the Hyper-HMM, that simultaneously aligns both temporal and spatial features across brains.