Results 21 to 30 of about 24,886 (258)
Fused Gromov-Wasserstein Distance for Structured Objects
Algorithms, 2020 Optimal transport theory has recently found many applications in machine learning thanks to its capacity to meaningfully compare various machine learning objects that are viewed as distributions.
Titouan Vayer +4 more
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Remote Sensing, 2023 The inversion of acoustic field data to estimate geoacoustic parameters has been a prominent research focus in the field of underwater acoustics for several decades.
Jiaqi Ding +3 more
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Dataset Distillation via the Wasserstein Metric
CoRR, 2023 Dataset Distillation (DD) aims to generate a compact synthetic dataset that enables models to achieve performance comparable to training on the full large dataset, significantly reducing computational costs. Drawing from optimal transport theory, we introduce WMDD (Wasserstein Metric-based Dataset Distillation), a straightforward yet powerful method ...
Liu, Haoyang +7 more
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Open-Set Signal Recognition Based on Transformer and Wasserstein Distance
Applied Sciences, 2023 Open-set signal recognition provides a new approach for verifying the robustness of models by introducing novel unknown signal classes into the model testing and breaking the conventional closed-set assumption, which has become very popular in real-world
Wei Zhang +4 more
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Well-posedness of a parabolic moving-boundary problem in the setting of Wasserstein gradient flows [PDF]
, 2010 We develop a gradient-flow framework based on the Wasserstein metric for a parabolic moving-boundary problem that models crystal dissolution and precipitation. In doing so we derive a new weak formulation for this moving-boundary problem and we show that
Peletier, Mark A., Portegies, Jacobus W.
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The exponential formula for the wasserstein metric [PDF]
ESAIM: Control, Optimisation and Calculus of Variations, 2016 Many evolutionary partial differential equations may be rewritten as the gradient flow of an energy functional, a perspective which provides useful estimates on the behavior of solutions. The notion of gradient flow requires both the specification of an energy functional and a metric with respect to which the gradient is taken.
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A generalization of Hausdorff dimension applied to Hilbert cubes and Wasserstein spaces [PDF]
, 2012 A Wasserstein spaces is a metric space of sufficiently concentrated probability measures over a general metric space. The main goal of this paper is to estimate the largeness of Wasserstein spaces, in a sense to be precised.
BENOÎT KLOECKNER +6 more
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Big Data and Cognitive Computing, 2022 Large retail companies routinely gather huge amounts of customer data, which are to be analyzed at a low granularity. To enable this analysis, several Key Performance Indicators (KPIs), acquired for each customer through different channels are associated
Andrea Ponti +4 more
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The Wasserstein metric in Factor Analysis [PDF]
, 2013 We consider the problem of approximating a (nonnegative definite) covariance matrix by the sum of two structured covariances –one which is diagonal and one which has low-rank. Such an additive decomposition follows the dictum of factor analysis where linear relations are sought between variables corrupted by independent measurement noise.
Lipeng Ning, Tryphon T. Georgiou
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The quadratic Wasserstein metric for inverse data matching [PDF]
Inverse Problems, 2020 Abstract This work characterizes, analytically and numerically, two major effects of the quadratic Wasserstein ( W 2 ) distance as the measure of data discrepancy in computational solutions of inverse problems.
Björn Engquist, Kui Ren, Yunan Yang
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