Gaussian processes for machine learning (GPML) toolbox

The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library of simple mean and covariance functions and mechanisms to compose more complex ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well as others suitable for classification. Finally, a range of inference methods is provided, including exact and variational inference, Expectation Propagation, and Laplace’s method dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks.

Multivariable toolbox for use with MATLAB.

The authors describe a toolbox for the frequency-domain analysis and design of multivariable feedback systems, to be used with PC-Matlab, or Pro-Matlab. The principal model representations used by the toolbox are described. Its capabilities are illustrated by a worked design example, which shows the use of a Nyquist array method. Other design techniques supported by the toolbox are briefly reviewed.

CACSD, GUI's, and the multivariable frequency domain toolbox

This talk describes a new version of the Multivariable Frequency Domain Toolbox for Matlab. The intellectual issue which arises here is whether there is a role for Matlab-4 GUI facilities in a Toolbox which provides relatively low-level functionality, with a correspondingly random pattern of user interaction. My belief is that there is a role, but it is very restricted: in effect only for providing convenient 'viewing' facilities for low-level objects (which are multivariable frequency responses in the case of the MFD Toolbox). There is a more obvious role for a GUI with higher-level functions, such as frequency domain identification or parametric controller optimisation.

Manopt, a matlab toolbox for optimization on manifolds

Optimization on manifolds is a rapidly developing branch of nonlinear optimization. Its focus is on problems where the smooth geometry of the search space can be leveraged to design effcient numerical algorithms. In particular, optimization on manifolds is well-suited to deal with rank and orthogonality constraints. Such structured constraints appear pervasively in machine learning applications, including low-rank matrix completion, sensor network localization, camera network registration, independent component analysis, metric learning, dimensionality reduction and so on. The Manopt toolbox, available at www.manopt.org, is a user-friendly, documented piece of software dedicated to simplify experimenting with state of the art Riemannian optimization algorithms. By dealing internally with most of the differential geometry, the package aims particularly at lowering the entrance barrier. © 2014 Nicolas Boumal.

The Graph Analysis Toolbox: Image Processing on Arbitrary Graphs

Office of Naval Research (N00014-01-1-0624)

A matlab toolbox for simulating transputer applications

The performance demands of modern control and signal processing systems is increasing beyond the capacity of conventional sequential processors, requiring parallel processing solutions to satisfy the real-time requirements.