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Found 7 papers, 2 papers with code
Estimating uterine activity from electrohysterogram measurements via statistical tensor decomposition
To facilitate the analysis of EHGs, we separate these measurements into uterine activity that is more variable across different electrodes and over time, which we term localized activity, and activity that is more evenly distributed in space and time.
KerGM: Kernelized Graph Matching
Graph matching plays a central role in such fields as computer vision, pattern recognition, and bioinformatics.
Sparsity-Assisted Signal Denoising and Pattern Recognition in Time-Series Data
We address the problem of signal denoising and pattern recognition in processing batch-mode time-series data by combining linear time-invariant filters, orthogonal multiresolution representations, and sparsity-based methods.
RetGK: Graph Kernels based on Return Probabilities of Random Walks
Graph-structured data arise in wide applications, such as computer vision, bioinformatics, and social networks.
Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation
Domain shift, which occurs when there is a mismatch between the distributions of training (source) and testing (target) datasets, usually results in poor performance of the trained model on the target domain.
Fast Kernel Learning for Multidimensional Pattern Extrapolation
This difficulty is compounded by the fact that Gaussian processes are typically only tractable for small datasets, and scaling an expressive kernel learning approach poses different challenges than scaling a standard Gaussian process model.
GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes
We introduce a new Bayesian nonparametric framework -- GPatt -- enabling automatic pattern extrapolation with Gaussian processes on large multidimensional datasets.
