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05/03/2023
A Statistical Exploration of Text Partition Into Constituents: The Case of the Priestly Source in the Books of Genesis and Exodus
We present a pipeline for a statistical textual exploration, offering a ...
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09/07/2022
Manifold Free Riemannian Optimization
Riemannian optimization is a principled framework for solving optimizati...
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01/23/2022
Mixed X-Ray Image Separation for Artworks with Concealed Designs
In this paper, we focus on X-ray images of paintings with concealed sub-...
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07/28/2021
Neural Network Approximation of Refinable Functions
In the desire to quantify the success of neural networks in deep learnin...
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07/13/2021
Convergence rates of vector-valued local polynomial regression
Non-parametric estimation of functions as well as their derivatives by m...
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05/11/2021
Non-Parametric Estimation of Manifolds from Noisy Data
A common observation in data-driven applications is that high dimensiona...
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09/16/2020
Image Separation with Side Information: A Connected Auto-Encoders Based Approach
X-radiography (X-ray imaging) is a widely used imaging technique in art ...
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07/20/2020
Approximating the Riemannian Metric from Point Clouds via Manifold Moving Least Squares
The approximation of both geodesic distances and shortest paths on point...
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07/28/2019
Approximating the Span of Principal Components via Iterative Least-Squares
In the course of the last century, Principal Component Analysis (PCA) ha...
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05/27/2019
Expression of Fractals Through Neural Network Functions
To help understand the underlying mechanisms of neural networks (NNs), s...
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11/02/2017
Approximation of Functions over Manifolds: A Moving Least-Squares Approach
We present an algorithm for approximating a function defined over a d-di...
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06/22/2016
Manifold Approximation by Moving Least-Squares Projection (MMLS)
In order to avoid the curse of dimensionality, frequently encountered in...
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02/23/2016