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

Thisperspectiveleadstoanovel construction via greedy optimization, and also provides a unifying informationgeometric viewofpresent andpastmethods. TheproposedRiemannian coreset construction algorithm is fully automated, requiring no problem-specific inputs aside from theprobabilistic model and dataset.

The success of contrastive learning is well known to be dependent on data augmentation.

Applications of optimal transport have recently gained remarkable attention as a result of the computational advantages of entropic regularization. However, in most situations the Sinkhorn approximation to the Wasserstein distance is replaced by a regularized version that is less accurate but easy to differentiate. In this work we characterize the differential properties of the original Sinkhorn approximation, proving that it enjoys the same smoothness of its regularized version and we explicitly provide an efficient algorithm to compute its gradient. We show that this result benefits both theory and applications: on one hand, high order smoothness confers statistical guarantees to learning with Wasserstein approximations. On the other hand, the gradient formula is used to efficiently solve learning and optimization problems in practice. Promising preliminary experiments complement our analysis.

While conceptually simple and easy to train, Imagen yields surprisingly strong results. Imagen outperforms other methods on COCO [38] with zero-shot FID-30K of 7.27, significantly outperforming prior work such asGLIDE [43](at 12.4) and the concurrent work ofDALL-E 2[56](at 10.4).