Graph Embedded Nonparametric Mutual Information For Supervised Dimensionality Reduction

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Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for dimensionality reduction of time-series using contrastive learning. A method can include receiving multidimensional input time series data that includes ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

Isogeometric analysis is a powerful paradigm which exploits the high smoothness of splines for the numerical solution of high order partial differential equations. However, the tensor-product structure of standard multivariate B-spline models is not well s ...

Since the birth of Information Theory, researchers have defined and exploited various information measures, as well as endowed them with operational meanings. Some were born as a "solution to a problem", like Shannon's Entropy and Mutual Information. Other ...

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised le ...

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Graph Embedded Nonparametric Mutual Information For Supervised Dimensionality Reduction

Chattez avec Graph Search

Density estimation in RKHS with application to Korobov spaces in high dimensions

Dimensionality reduction of time-series data, and systems and devices that use the resultant embeddings

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Adaptive isogeometric methods with C-1 (truncated) hierarchical splines on planar multi-patch domains

A Functional Perspective on Information Measures

A structured prediction approach for robot imitation learning

Structure-preserving approaches and data-driven closure modeling for model order reduction

Information-driven transitions in projections of underdamped dynamics

Virtual metrology applied to milling process

Characterization of High Harmonic Frequencies in Reactor Noise Experiments Within the CORTEX Project

Mutual information in changing environments: Nonlinear interactions, out-of-equilibrium systems, and continuously varying diffusivities

A machine learning approach to portfolio pricing and risk management for high-dimensional problems

The Gray-Wyner Network and Wyner's Common Information for Gaussian Sources

Singularities of general fibers and the LMMP

Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models