Categories: FAANG

Low-Rank Optimal Transport: Approximation, Statistics and Debiasing

The matching principles behind optimal transport (OT) play an increasingly important role in machine learning, a trend which can be observed when OT is used to disambiguate datasets in applications (e.g. single-cell genomics) or used to improve more complex methods (e.g. balanced attention in transformers or self-supervised learning). To scale to more challenging problems, there is a growing consensus that OT requires solvers that can operate on millions, not thousands, of points. The low-rank optimal transport (LOT) approach advocated in (Scetbon et al., 2021) holds several promises in that…
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Continuing the music video u/optimisoprimeo posted: https://www.reddit.com/r/StableDiffusion/comments/1t64gni/so_far_this_is_my_favorite_usecase_for_ltx/ submitted by /u/hidden2u [link] [comments]

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