Results 71 to 80 of about 942,483 (302)
Diffusion model conditioning on Gaussian mixture model and negative Gaussian mixture gradient
NeurocomputingDiffusion 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 +5 more
openaire +2 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Choroid plexus volume (CPV) has been proposed as a neuro‐immunological marker of multiple sclerosis (MS), but its relevance in myelin oligodendrocyte glycoprotein antibody–associated disease (MOGAD) remains uncertain. We analyzed CPV in 43 individuals with MOGAD, 48 with MS, and 44 healthy controls using a Bayesian Gaussian mixture modeling ...
Jae‐Won Hyun +4 more
wiley +1 more source
Kinect posture reconstruction based on a local mixture of Gaussian process models [PDF]
, 2016 Depth sensor based 3D human motion estimation hardware such as Kinect has made interactive applications more popular recently. However, it is still challenging to accurately recognize postures from a single depth camera due to the inherently noisy data ...
Shum, Hubert P.H. +4 more
core +1 more source
On the Properties of Gaussian Copula Mixture Models
Advances in Artificial Intelligence and Machine Learning, 2023 11 pages paper for theoretical properties and new algorithms for ...
Ke Wan 0001, Alain L. Kornhauser
openaire +3 more sources
White Matter and Perivascular Imaging Changes in Alzheimer's Disease and Cerebral Amyloid Angiopathy
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Peak‐width of skeletonized mean diffusivity (PSMD) and diffusion tensor imaging–analysis along the perivascular space (DTI‐ALPS), reflecting white matter integrity and glymphatic function, are altered in Alzheimer's disease (AD).
Debina Laishram +3 more
wiley +1 more source
gmm_diag and gmm_full: C++ classes for multi-threaded Gaussian mixture models and expectation-maximisation [PDF]
, 2017 Statistical modelling of multivariate data through a convex mixture of Gaussians, also known as a Gaussian mixture model (GMM), has many applications in fields such as signal processing, econometrics, and pattern recognition (Bishop 2006). Each component
Sanderson, Conrad +3 more
core +1 more source
Gaussian mixture model of heart rate variability. [PDF]
PLoS ONE, 2012 Heart rate variability (HRV) is an important measure of sympathetic and parasympathetic functions of the autonomic nervous system and a key indicator of cardiovascular condition.
Tommaso Costa +2 more
doaj +1 more source
dynoGP: Deep Gaussian Processes for Dynamic System Identification
International Journal of Adaptive Control and Signal Processing, EarlyView.This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +2 more
wiley +1 more source
Parallelized Particle and Gaussian Sum Particle Filters for Large Scale Freeway Traffic Systems [PDF]
, 2012 Large scale traffic systems require techniques able to: 1) deal with high amounts of data and heterogenous data coming from different types of sensors, 2) provide robustness in the presence of sparse sensor data, 3) incorporate different models that can ...
Gning, Amadou +7 more
core +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 +4 more
wiley +1 more source