Results 61 to 70 of about 24,886 (258)
Three superposition principles: currents, continuity equations and curves of measures
, 2015 We establish a general superposition principle for curves of measures solving a continuity equation on metric spaces without any smooth structure nor underlying measure, representing them as marginals of measures concentrated on the solutions of the ...
Stepanov, Eugene, Trevisan, Dario
core +1 more source
Statistical Learning in Wasserstein Space [PDF]
, 2020 We seek a generalization of regression and principle component analysis (PCA) in a metric space where data points are distributions metrized by the Wasserstein metric. We recast these analyses as multimarginal optimal transport problems.
Georgiou, TT, Karimi, A, Ripani, L
core +1 more source
Artificial Intelligence for Bone: Theory, Methods, and Applications
Advanced Intelligent Discovery, EarlyView.Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan +3 more
wiley +1 more source
Quantization-based Bounds on the Wasserstein Metric
CoRR23 pages, 8 figures, 7 ...
Jonathan Bobrutsky, Amit Moscovich
openaire +2 more sources
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Advanced Intelligent Discovery, EarlyView.Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
Canonical Variates in Wasserstein Metric Space
CoRRdouble space 37 pages, 6 ...
Jia Li, Lin Lin
openaire +2 more sources
(q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs
CoRR, 2019 Generative Adversial Networks (GANs) have made a major impact in computer vision and machine learning as generative models. Wasserstein GANs (WGANs) brought Optimal Transport (OT) theory into GANs, by minimizing the $1$-Wasserstein distance between model and data distributions as their objective function.
Anton Mallasto +3 more
openaire +2 more sources
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
American Journal of Medical Genetics Part A, EarlyView.ABSTRACT People with Phelan–McDermid syndrome (PMS) have reduced speech and language abilities, yet little research has profiled the communication abilities in this population. The purpose of this study was threefold: identifying the language and communication profiles of school‐aged children with PMS, identifying genetic contributions to language and ...
Sarah Quadri‐Valverde +12 more
wiley +1 more source