Results 81 to 90 of about 2,509,053 (235)

Augmented Sliced Wasserstein Distances

, 2020
While theoretically appealing, the application of the Wasserstein distance to large-scale machine learning problems has been hampered by its prohibitive computational cost. The sliced Wasserstein distance and its variants improve the computational efficiency through the random projection, yet they suffer from low accuracy if the number of projections ...
Chen, Xiongjie, Yang, Yongxin, Li, Yunpeng   +2 more
openaire   +2 more sources

Inverse Design of Alloys via Generative Algorithms: Optimization and Diffusion within Learned Latent Space

Advanced Intelligent Discovery, EarlyView.
This work presents a novel generative artificial intelligence (AI) framework for inverse alloy design through operations (optimization and diffusion) within learned compact latent space from variational autoencoder (VAE). The proposed work addresses challenges of limited data, nonuniqueness solutions, and high‐dimensional spaces.
Mohammad Abu‐Mualla, Ellis Crabtree, Fredrick Michael, Yayue Pan, Jida Huang   +4 more
wiley   +1 more source

Geometrical Insights for Implicit Generative Modeling

, 2019
Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean ...
A Auffinger, A Gretton, A Müller, AA Zinger, B Schölkopf, B Sriperumbudur, BK Sriperumbudur, BK Sriperumbudur, C Villani, D Sejdinovic, GJ Székely, H Cramér, IJ Schoenberg, JM Hammersley, MA Aizerman, N Aronszajn, N Fournier, P Milgrom, R Mises von, RJ Serfling, RM Neal, ST Rachev, Steffen Dereich, T Hastie, VS Borkar, X Nguyen   +25 more
core   +1 more source

Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis [PDF]

Neurocomputing, 2019
The demand of artificial intelligent adoption for condition-based maintenance strategy is astonishingly increased over the past few years. Intelligent fault diagnosis is one critical topic of maintenance solution for mechanical systems.
Cheng Cheng, Beitong Zhou, Guijun Ma, Dongrui Wu, Ye Yuan   +4 more
semanticscholar   +1 more source

Supervised Tree-Wasserstein Distance

, 2021
To measure the similarity of documents, the Wasserstein distance is a powerful tool, but it requires a high computational cost. Recently, for fast computation of the Wasserstein distance, methods for approximating the Wasserstein distance using a tree metric have been proposed.
Takezawa, Yuki, Sato, Ryoma, Yamada, Makoto   +2 more
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, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Survey of Distances between the Most Popular Distributions

Analytics, 2023
We present a number of upper and lower bounds for the total variation distances between the most popular probability distributions. In particular, some estimates of the total variation distances in the cases of multivariate Gaussian distributions ...
Mark Kelbert
doaj   +1 more source

First and second moments for self-similar couplings and Wasserstein distances

, 2014
We study aspects of the Wasserstein distance in the context of self-similar measures. Computing this distance between two measures involves minimising certain moment integrals over the space of \emph{couplings}, which are measures on the product space ...
Fraser, Jonathan M.
core   +1 more source

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

Full-Waveform Inversion of Two-Parameter Ground-Penetrating Radar Based on Quadratic Wasserstein Distance

Remote Sensing
Full-waveform inversion (FWI) is one of the most promising techniques in current ground-penetrating radar (GPR) inversion methods. The least-squares method is usually used, minimizing the mismatch between the observed signal and the simulated signal ...
Kai Lu, Yibo Wang, Heting Han, Shichao Zhong, Yikang Zheng   +4 more
doaj   +1 more source