Results 71 to 80 of about 298,891 (271)

Consistency, breakdown robustness, and algorithms for robust improper maximum likelihood clustering [PDF]

, 2017
The robust improper maximum likelihood estimator (RIMLE) is a new method for robust multivariate clustering finding approximately Gaussian clusters.
Coretto, Pietro, Hennig, Christian
core   +1 more source

Diffusion model conditioning on Gaussian mixture model and negative Gaussian mixture gradient

Neurocomputing
Diffusion models (DMs) are a type of generative model that has a huge impact on image synthesis and beyond. They achieve state-of-the-art generation results in various generative tasks. A great diversity of conditioning inputs, such as text or bounding boxes, are accessible to control the generation.
Weiguo Lu, Xuan Wu, Deng Ding, Jinqiao Duan, Jirong Zhuang, Gangnan Yuan   +5 more
openaire   +2 more sources

A Fast Incremental Gaussian Mixture Model

PLOS ONE, 2015
Ce travail s'appuie sur les efforts antérieurs en matière d'apprentissage incrémentiel en ligne, à savoir le Incremental Gaussian Mixture Network (IGMN). L'IGMN est capable d'apprendre des flux de données en une seule passe en améliorant son modèle après avoir analysé chaque point de données et l'avoir jeté par la suite.
Rafael Pinto, Paulo Martins Engel
openaire   +5 more sources

Dielectric Barrier Discharge Plasma Deoxidation of Natively Oxide Layer of Copper Powders in a Fluidized Bed

Advanced Engineering Materials, EarlyView.
This paper presents a novel approach to reducing oxide layers on metal powders using low‐temperature hydrogen dielectric barrier discharge plasmas at atmospheric pressure. Unlike conventional hydrogen‐plasma reductions, the powders do not contact the plasma directly.
Shukang Zhang, Annett Wollmann, Thomas Gimpel, Alfred P. Weber   +3 more
wiley   +1 more source

GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models

Journal of Statistical Software, 2016
Methods for clustering in unsupervised learning are an important part of the statistical toolbox in numerous scientific disciplines. Tewari, Giering, and Raghunathan (2011) proposed to use so-called Gaussian mixture copula models (GMCM) for general ...
Anders Ellern Bilgrau, Poul Svante Eriksen, Jakob Gulddahl Rasmussen, Hans Erik Johnsen, Karen Dybkaer, Martin Boegsted   +5 more
doaj   +1 more source

Sliced Wasserstein Distance for Learning Gaussian Mixture Models

, 2017
Gaussian mixture models (GMM) are powerful parametric tools with many applications in machine learning and computer vision. Expectation maximization (EM) is the most popular algorithm for estimating the GMM parameters.
Hoffmann, Heiko, Kolouri, Soheil, Rohde, Gustavo K.   +2 more
core   +1 more source

Bayesian approaches to Gaussian mixture modeling

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998
A Bayesian-based methodology is presented which automatically penalizes overcomplex models being fitted to unknown data. We show that, with a Gaussian mixture model, the approach is able to select an "optimal" number of components in the model and so partition data sets.
Roberts, S, Husmeier, D, Rezek, I, Penny, W   +3 more
openaire   +2 more sources

Surface Tension Measurement of Ti‐6Al‐4V by Falling Droplet Method in Oxygen‐Free Atmosphere

Advanced Engineering Materials, EarlyView.
In this article, the temperature‐dependent surface tension of free falling, oscillating Ti‐6Al‐4V droplets is investigated in both argon and monosilane doped, oxygen‐free atmosphere. Droplet temperature and oscillation are captured with one single high‐speed camera, and the surface tension is calculated with Rayleigh's formula.
Johannes May, Finn‐Lennard Janthur, Lena Kreie, Michael Müller, Sarah Seffer, Nick Schwarz, Jörg Hermsdorf, Thomas Hassel, Stefan Kaierle, Ludger Overmeyer   +9 more
wiley   +1 more source

Multimodal Data‐Driven Microstructure Characterization

Advanced Engineering Materials, EarlyView.
A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang, Santiago Benito, Özge Özgün, Sebastian Weber, Markus Stricker   +4 more
wiley   +1 more source

In Situ Micromechanical Study of Bimodal γ′–γ″ Precipitate Assemblies in Ni–Cr–Al–Nb Superalloy

Advanced Engineering Materials, EarlyView.
A Ni–Cr–Al–Nb superalloy with a bimodal γ′–γ″ precipitate distribution is developed. Composite precipitate assemblies form through heterogeneous nucleation, effectively impeding dislocation motion. Micropillar compression reveals high strength at room and elevated temperatures, governed by precipitate shearing, with coupled faulting mechanisms ...
Ujjval Bansal, Tae‐Hyeok Kang, Pyuck‐Pa Choi, Subin Lee, Christoph Kirchlechner   +4 more
wiley   +1 more source