Results 131 to 140 of about 2,509,053 (235)
Plant Biotechnology Journal, EarlyView.ABSTRACT Genomic selection (GS) is critical for accelerating genetic gain in modern plant breeding. Deep learning approaches offer powerful non‐linear representation capabilities for modelling non‐additive effects. However, their application in GS remains restricted, as high‐dimensional, low‐sample and noisy data hinder the identification of ...
Yuexin Ma +7 more
wiley +1 more source
International Journal of Computer Science in SportStudies to understand the shooting preferences of basketball players relied exclusively on data on shot location, which did not lead to concrete understandings because they contained no information on how they moved to that location.
Yamada Kazuhiro, Fujii Keisuke
doaj +1 more source
Fast Estimation of Wasserstein Distances via Regression on Sliced Wasserstein Distances
We address the problem of efficiently computing Wasserstein distances for multiple pairs of distributions drawn from a meta-distribution. To this end, we propose a fast estimation method based on regressing Wasserstein distance on sliced Wasserstein (SW) distances.
Nguyen, Khai, Nguyen, Hai, Ho, Nhat
openaire +2 more sources
Spatial depth for data in metric spaces
Scandinavian Journal of Statistics, EarlyView.Abstract We propose a novel measure of statistical depth, the metric spatial depth, for data residing in an arbitrary metric space. The measure assigns high (low) values for points located near (far away from) the bulk of the data distribution, allowing quantifying their centrality/outlyingness.
Joni Virta
wiley +1 more source
MathematicsWhat if machines could seamlessly translate between the visual richness of images and the semantic depth of language with mathematical precision? This paper presents a theoretical and empirical analysis of five novel cross-modal Wasserstein adversarial ...
Joseph Tafataona Mtetwa +2 more
doaj +1 more source
In‐and‐Out: Algorithmic Diffusion for Sampling Convex Bodies
Random Structures &Algorithms, Volume 68, Issue 3, May 2026.ABSTRACT We present a new random walk for uniformly sampling high‐dimensional convex bodies. It achieves state‐of‐the‐art runtime complexity with stronger guarantees on the output than previously known, namely in Rényi divergence (which implies TV, 𝒲2, KL, χ2$$ {\chi}^2 $$).
Yunbum Kook +2 more
wiley +1 more source
Distance-Based Tree-Sliced Wasserstein Distance
To overcome computational challenges of Optimal Transport (OT), several variants of Sliced Wasserstein (SW) has been developed in the literature. These approaches exploit the closed-form expression of the univariate OT by projecting measures onto (one-dimensional) lines.
Tran, Hoang V. +5 more
openaire +2 more sources
A New Clustering Strategy for Geo‐Referenced Time Series Based on Optimal Transport
Environmetrics, Volume 37, Issue 3, April 2026.ABSTRACT Advances in spatio‐temporal data collection have created a demand for efficient methods to analyze geo‐referenced time series (GTS), which capture changes over time at specific spatial locations. Traditional clustering methods often struggle to handle the high‐dimensional, complex nature of GTS.
Pasquale Pipiciello +2 more
wiley +1 more source
Towards Analysis of Covariance Descriptors via Bures–Wasserstein Distance
MathematicsA brain–computer interface (BCI) provides a direct communication pathway between the human brain and external devices, enabling users to control them through thought.
Huajun Huang +4 more
doaj +1 more source
Stable and Fast Deep Mutual Information Maximization Based on Wasserstein Distance. [PDF]
Entropy (Basel), 2023 He X, Peng C, Wang L, Tan W, Wang Z.
europepmc +1 more source