The proposed MLP decoder aggregates information from different layers, andthus combining both local attention and global attention to render powerful representations.

When n is independent ofδ our approach yields an algorithm whose sample complexityconvergesto 2n2 log 1δ asngrows.

But these methods are unable to improve throughput (frames-per-second) on real-life hardware while simultaneously preserving robustness toadversarial perturbations.

As inthe classical problem, weights are fixed by an adversary and elements appear in random order. In contrast to previous variants of predictions, our algorithm only has access toamuch weakerpiece ofinformation: anadditive gapc.

To decouple the learning of underlying scene geometry from dynamic motion, we represent the scene as a time-invariantsigneddistance function (SDF)whichservesasareference frame, along with a time-conditioned deformation field.