18 citations · 63 across the 15 of their papers we have counts for
17 papers
Learning Hidden Ecological and Epidemic Processes in a Reduced-Order Space
Maria Vasilyeva, Zheng Wei, Kelum Gajamannage +3
We study data-driven learning of ecological and epidemic dynamics governed by nonlinear reaction and reaction--diffusion systems. We consider both neural approaches for learning th…
Efficient and Robust Remote Sensing Image Denoising Using Randomized Approximation of Geodesics' Gramian on the Manifold Underlying the Patch Space
Kelum Gajamannage, Dilhani I. Jayathilake, Maria Vasilyeva
Remote sensing images are widely utilized in many disciplines such as feature recognition and scene semantic segmentation. However, due to environmental factors and the issues of t…
Multiscale method for image denoising using nonlinear diffusion process: local denoising and spectral multiscale basis functions
Maria Vasilyeva, Aleksei Krasnikov, Kelum Gajamannage +1
We consider image denoising using a nonlinear diffusion process, where we solve unsteady partial differential equations with nonlinear coefficients. The noised image is given as an…
Fraud Detection Using Optimized Machine Learning Tools Under Imbalance Classes
Mary Isangediok, Kelum Gajamannage
Fraud detection is a challenging task due to the changing nature of fraud patterns over time and the limited availability of fraud examples to learn such sophisticated patterns. Th…
Efficient Image Denoising by Low-Rank Singular Vector Approximations of Geodesics' Gramian Matrix
Kelum Gajamannage, Yonggi Park, S. M. Mallikarjunaiah +1
With the advent of sophisticated cameras, the urge to capture high-quality images has grown enormous. However, the noise contamination of the images results in substandard expectat…
Real-time Forecasting of Time Series in Financial Markets Using Sequentially Trained Many-to-one LSTMs
Kelum Gajamannage, Yonggi Park
Financial markets are highly complex and volatile; thus, learning about such markets for the sake of making predictions is vital to make early alerts about crashes and subsequent r…
Geodesic Gramian Denoising Applied to the Images Contaminated With Noise Sampled From Diverse Probability Distributions
Yonggi Park, Kelum Gajamannage, Alexey Sadovski
As quotidian use of sophisticated cameras surges, people in modern society are more interested in capturing fine-quality images. However, the quality of the images might be inferio…
Recurrent Neural Networks for Dynamical Systems: Applications to Ordinary Differential Equations, Collective Motion, and Hydrological Modeling
Yonggi Park, Kelum Gajamannage, Dilhani I. Jayathilake +1
Classical methods of solving spatiotemporal dynamical systems include statistical approaches such as autoregressive integrated moving average, which assume linear and stationary re…
Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders
Kelum Gajamannage, Yonggi Park, Randy Paffenroth +1
Learning dynamics of collectively moving agents such as fish or humans is an active field in research. Due to natural phenomena such as occlusion and change of illumination, the mu…
Image Denoising Using the Geodesics' Gramian of the Manifold Underlying Patch-Space
Kelum Gajamannage
With the proliferation of sophisticated cameras in modern society, the demand for accurate and visually pleasing images is increasing. However, the quality of an image captured by…
Bounded Manifold Completion
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. In this pa…
Network Topology Mapping from Partial Virtual Coordinates and Graph Geodesics
Anura P. Jayasumana, Randy Paffenroth, Gunjan Mahindre +2
For many important network types (e.g., sensor networks in complex harsh environments and social networks) physical coordinate systems (e.g., Cartesian), and physical distances (e.…
A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics
Kelum Gajamannage, Randy Paffenroth, Erik M. Bollt
Existing dimensionality reduction methods are adept at revealing hidden underlying manifolds arising from high-dimensional data and thereby producing a low-dimensional representati…
Modeling the lowest-cost splitting of a herd of cows by optimizing a cost function
Kelum Gajamannage, Erik M. Bollt, Mason A. Porter +1
Animals live in groups to defend against predation and to obtain food. However, for some animals --- especially ones that spend long periods of time feeding --- there are costs if…
Detecting phase transitions in collective behavior using manifold's curvature
Kelum Gajamannage, Erik M. Bollt
If a given behavior of a multi-agent system restricts the phase variable to a invariant manifold, then we define a phase transition as change of physical characteristics such as sp…
Dimensionality Reduction of Collective Motion by Principal Manifolds
Kelum Gajamannage, Sachit Butail, Maurizio Porfiri +1
While the existence of low-dimensional embedding manifolds has been shown in patterns of collective motion, the current battery of nonlinear dimensionality reduction methods are no…
Identifying manifolds underlying group motion in Vicsek agents
Kelum Gajamannage, Sachit Butail, Maurizio Porfiri +1
Collective motion of animal groups often undergoes changes due to perturbations. In a topological sense, we describe these changes as switching between low-dimensional embedding ma…