Recent advances in optimal transport for machine learning
EF Montesuma, FMN Mboula… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Recently, Optimal Transport has been proposed as a probabilistic framework in Machine
Learning for comparing and manipulating probability distributions. This is rooted in its rich …
Learning for comparing and manipulating probability distributions. This is rooted in its rich …
Generative multiplane images: Making a 2d gan 3d-aware
What is really needed to make an existing 2D GAN 3D-aware? To answer this question, we
modify a classical GAN, ie., StyleGANv2, as little as possible. We find that only two …
modify a classical GAN, ie., StyleGANv2, as little as possible. We find that only two …
Generalized sliced wasserstein distances
The Wasserstein distance and its variations, eg, the sliced-Wasserstein (SW) distance, have
recently drawn attention from the machine learning community. The SW distance …
recently drawn attention from the machine learning community. The SW distance …
One loss for quantization: Deep hashing with discrete wasserstein distributional matching
Image hashing is a principled approximate nearest neighbor approach to find similar items
to a query in a large collection of images. Hashing aims to learn a binary-output function that …
to a query in a large collection of images. Hashing aims to learn a binary-output function that …
Statistical, robustness, and computational guarantees for sliced wasserstein distances
Sliced Wasserstein distances preserve properties of classic Wasserstein distances while
being more scalable for computation and estimation in high dimensions. The goal of this …
being more scalable for computation and estimation in high dimensions. The goal of this …
Projection‐based techniques for high‐dimensional optimal transport problems
Optimal transport (OT) methods seek a transformation map (or plan) between two probability
measures, such that the transformation has the minimum transportation cost. Such a …
measures, such that the transformation has the minimum transportation cost. Such a …
Semantic correspondence as an optimal transport problem
Establishing dense correspondences across semantically similar images is a challenging
task. Due to the large intra-class variation and background clutter, two common issues occur …
task. Due to the large intra-class variation and background clutter, two common issues occur …
Sliced optimal partial transport
Optimal transport (OT) has become exceedingly popular in machine learning, data science,
and computer vision. The core assumption in the OT problem is the equal total amount of …
and computer vision. The core assumption in the OT problem is the equal total amount of …
Tutorial on amortized optimization
B Amos - Foundations and Trends® in Machine Learning, 2023 - nowpublishers.com
Optimization is a ubiquitous modeling tool and is often deployed in settings which
repeatedly solve similar instances of the same problem. Amortized optimization methods …
repeatedly solve similar instances of the same problem. Amortized optimization methods …
[PDF][PDF] Statistical optimal transport
Statistical Optimal Transport arXiv:2407.18163v2 [math.ST] 7 Nov 2024 Page 1 Statistical
Optimal Transport Sinho Chewi Yale Jonathan Niles-Weed NYU Philippe Rigollet MIT …
Optimal Transport Sinho Chewi Yale Jonathan Niles-Weed NYU Philippe Rigollet MIT …