The Gaussian network model (GNM) is a representation of a biological macromolecule as an elastic mass-and-spring network to study, understand, and characterize...

typically involve training a neural network to sequentially denoise images blurred with Gaussian noise. The model is trained to reverse the process of...

A Neural Network Gaussian Process (NNGP) is a Gaussian process (GP) obtained as the limit of a certain type of sequence of neural networks. Specifically...

sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In...

and modelling, Gaussian noise is used as additive white noise to generate additive white Gaussian noise. In telecommunications and computer networking, communication...

representation using 3D Gaussians to model radiance fields, along with an interleaved optimization and density control of the Gaussians. A fast visibility-aware...

generalization of multivariate normal distributions. Gaussian processes are useful in statistical modelling, benefiting from properties inherited from the normal...

pioneering work of Tirion (1996), succeeded by the development of the Gaussian network model (GNM) (Bahar et al., 1997; Haliloglu et al., 1997), and by the work...

integral Gaussian variogram model Gaussian mixture model Gaussian network model Gaussian noise Gaussian smoothing Gaussian splatting The inverse Gaussian distribution...

network Dynamic network analysis Dynamic single-frequency networks Gaussian network model Gene regulatory network Gradient network Network planning and design...

laboratory. Modeling techniques include differential equations (ODEs), Boolean networks, Petri nets, Bayesian networks, graphical Gaussian network models, Stochastic...

In mathematics, Gaussian elimination, also known as row reduction, is an algorithm for solving systems of linear equations. It consists of a sequence of...

Games 'n' Music Gaussian network model Gerakan Nelajan Marhaenis Germanisches Nationalmuseum GNM (API) German New Medicine (Germanische Neue Medizin) a...

processing, a Gaussian filter is a filter whose impulse response is a Gaussian function (or an approximation to it, since a true Gaussian response would...

Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian) on training images....

data. This idea has been demonstrated in by using Gaussian priors, whereby a Gaussian process model with ESN-driven kernel function is obtained. Such...

learning, Gaussian process approximation is a computational method that accelerates inference tasks in the context of a Gaussian process model, most commonly...

A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a...

\theta _{g}=(\mu _{g},\Sigma _{g})} . This defines a Gaussian mixture model. The parameters of the model, τ g {\displaystyle \tau _{g}} and θ g {\displaystyle...

between those points and the new, unobserved point. Gaussian processes are popular surrogate models in Bayesian optimisation used to do hyperparameter...

{\displaystyle x_{3}=5} , leaving the second spring slack. Gaussian network model Anisotropic Network Model Stiffness matrix Spring-mass system Laplacian matrix...

attractive alternative. Normal mode analysis with elastic network models, such as the Gaussian network model, can be used to probe molecular dynamics trajectories...

In mathematics, a Gaussian function, often simply referred to as a Gaussian, is a function of the base form f ( x ) = exp ⁡ ( − x 2 ) {\displaystyle f(x)=\exp(-x^{2})}...

Gaussian adaptation (GA), also called normal or natural adaptation (NA) is an evolutionary algorithm designed for the maximization of manufacturing yield...

previously, scalable copula models for large dimensions only allowed the modelling of elliptical dependence structures (i.e., Gaussian and Student-t copulas)...

since finite width neural networks often perform strictly better as layer width is increased. The Neural Network Gaussian Process (NNGP) corresponds...

some of the simplest Bayesian network models. Naive Bayes classifiers generally perform worse than more advanced models like logistic regressions, especially...

(typically from a Gaussian distribution). Hidden Markov models can also be generalized to allow continuous state spaces. Examples of such models are those where...

electrical network is an interconnection of electrical components (e.g., batteries, resistors, inductors, capacitors, switches, transistors) or a model of such...

reactive (NO, NO2, O3) gases from a road network of line sources on a local scale. It is a Gaussian line source model which includes an analytical solution...