Search Results for author: Randy Paffenroth

Found 7 papers, 0 papers with code

A Pre-training Oracle for Predicting Distances in Social Networks

no code implementations6 Jun 2021 Gunjan Mahindre, Randy Paffenroth, Anura Jayasumana, Rasika Karkare

OSP can be easily extended to other domains such as random networks by choosing an appropriate model to generate synthetic training data, and therefore promises to impact many different network learning problems.

Low-Rank Matrix Completion

Blind Image Denoising and Inpainting Using Robust Hadamard Autoencoders

no code implementations26 Jan 2021 Rasika Karkare, Randy Paffenroth, Gunjan Mahindre

Herein we demonstrate these techniques on standard machine learning tasks, such as image inpainting and denoising for the MNIST and CIFAR10 datasets.

Anomaly Detection Image Denoising +2

Machine Learning in LiDAR 3D point clouds

no code implementations22 Jan 2021 F. Patricia Medina, Randy Paffenroth

For instance, we observe that combining feature engineering with a dimension reduction a method such as PCA, there is an improvement in the accuracy of the classification with respect to doing a straightforward classification with the raw data.

Classification Dimensionality Reduction +2

A Patch-based Image Denoising Method Using Eigenvectors of the Geodesics' Gramian Matrix

no code implementations14 Oct 2020 Kelum Gajamannage, Randy Paffenroth, Anura P. Jayasumana

Thus, here we propose a novel and computationally efficient image denoising method that is capable of producing an accurate output.

Image Denoising

Bounded Manifold Completion

no code implementations19 Dec 2019 Kelum Gajamannage, Randy Paffenroth

Nonlinear dimensionality reduction or, equivalently, the approximation of high-dimensional data using a low-dimensional nonlinear manifold is an active area of research.

Dimensionality Reduction Image Inpainting +2

Dimension Estimation Using Autoencoders

no code implementations24 Sep 2019 Nitish Bahadur, Randy Paffenroth

In DE, one attempts to estimate the intrinsic dimensionality or number of latent variables in a set of measurements of a random vector.

Dimensionality Reduction

A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics

no code implementations21 Jul 2017 Kelum Gajamannage, Randy Paffenroth, Erik M. Bollt

Herein, we propose a framework for nonlinear dimensionality reduction that generates a manifold in terms of smooth geodesics that is designed to treat problems in which manifold measurements are either sparse or corrupted by noise.

Dimensionality Reduction

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