This manuscript provides valuable information on the neurodynamics of emotional processing while participants were watching movie clips. The methods and results were solid in deciphering the ...

Additionally, CREAM divides the pre-trained context window into three segments (head, middle, tail) and uses a truncated Gaussian function to prioritize the middle segment during fine-tuning.

As shape, I chose a Gaussian function, isotropic in the two spatial dimensions, which was found to nicely fit the probability distribution function of the position of the real impinging point relative ...

Gaussian processes, known to have versatile uses in several fields across engineering, science, economics, show important advantages to several alternative approaches while controlling model ...

A Fast Method for Fitting a Multidimensional Gaussian Function ... parameters of the covariance matrix and the linear profile scale for the given initial centroid position for arbitrary dimensions. ..

The covariance selection in Gaussian graphical models consists in selecting, based on a sample of a multivariate normal vector, all those pairs of variables that are conditionally dependent given the ...

While researchers have traditionally employed Gaussian processes (GP) for specifying prior and posterior distributions over functions, this approach becomes computationally expensive when scaled, is ...

Currently, training of shape-GMMs is recommended for systems (or subsystems) that can be represented by ≲200 particles and ≲100k configurations to estimate high-dimensional covariance matrices and ...

To get started, you may want to check out the demo notebook that estimates the proposed R-D sandwich bounds on a 2D Gaussian source, and compares with the BA algorithm and the analytical R-D function.