Results 41 to 50 of about 33,205 (174)
Advanced Intelligent Discovery, EarlyView.We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob +2 more
wiley +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
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
Explainable AI Using the Wasserstein Distance
IEEE AccessAI-based decision systems often lack transparency due to their black-box nature and lack explanations of their decisions, which are crucial for life-changing applications such as disease diagnosis, financial investments, and military decisions ...
Shion Samadder Chaudhury +2 more
doaj +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
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 +25 more
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
Hyperspectral Anomaly Detection Based on Wasserstein Distance and Spatial Filtering
Remote Sensing, 2022 Since anomaly targets in hyperspectral images (HSIs) with high spatial resolution appear as connected areas instead of single pixels or subpixels, both spatial and spectral information of HSIs can be exploited for a hyperspectal anomaly detection (AD ...
Xiaoyu Cheng +3 more
doaj +1 more source
2D Implementation of Kinetic‐Diffusion Monte Carlo in Eiron
Contributions to Plasma Physics, EarlyView.ABSTRACT Particle‐based kinetic Monte Carlo simulations of neutral particles are one of the major computational bottlenecks in tokamak scrape‐off layer simulations. This computational cost comes from the need to resolve individual collision events in high‐collisional regimes.
Oskar Lappi +3 more
wiley +1 more source
Remote SensingFull-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 +4 more
doaj +1 more source