Matthew Thorpe

Orcid: 0000-0003-2480-5404

According to our database1, Matthew Thorpe authored at least 28 papers between 2014 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

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Bibliography

2024
A linear transportation Lp distance for pattern recognition.
Pattern Recognit., March, 2024

2023
Navigating the development challenges in creating complex data systems.
Nat. Mac. Intell., July, 2023

Manifold learning in Wasserstein space.
CoRR, 2023

Discrete-to-Continuum Rates of Convergence for $p$-Laplacian Regularization.
CoRR, 2023

PTL<sup>p</sup>: Partial Transport L<sup>p</sup> Distances.
CoRR, 2023

Sliced Optimal Partial Transport.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
The Linearized Hellinger-Kantorovich Distance.
SIAM J. Imaging Sci., 2022

Rates of Convergence for Regression with the Graph Poly-Laplacian.
CoRR, 2022

Classification of datasets with imputed missing values: does imputation quality matter?
CoRR, 2022

Γ-Convergence of an Ambrosio-Tortorelli approximation scheme for image segmentation.
CoRR, 2022

GRAND++: Graph Neural Diffusion with A Source Term.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans.
Nat. Mach. Intell., 2021

Robust Certification for Laplace Learning on Geometric Graphs.
Proceedings of the Mathematical and Scientific Machine Learning, 2021

2020
A Linear Transportation L<sup>p</sup> Distance for Pattern Recognition.
CoRR, 2020

Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: a systematic methodological review.
CoRR, 2020

Rates of Convergence for Laplacian Semi-Supervised Learning with Low Labeling Rates.
CoRR, 2020

From graph cuts to isoperimetric inequalities: Convergence rates of Cheeger cuts on data clouds.
CoRR, 2020

Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Analysis of p-Laplacian Regularization in Semisupervised Learning.
SIAM J. Math. Anal., 2019

PDE-Inspired Algorithms for Semi-Supervised Learning on Point Clouds.
CoRR, 2019

2018
Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithms.
CoRR, 2018

Representing and Learning High Dimensional Data With the Optimal Transport Map From a Probabilistic Viewpoint.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017
Optimal Mass Transport: Signal processing and machine-learning applications.
IEEE Signal Process. Mag., 2017

A Transportation L<sup>p</sup> Distance for Signal Analysis.
J. Math. Imaging Vis., 2017

Analysis of $p$-Laplacian Regularization in Semi-Supervised Learning.
CoRR, 2017

2016
Transport-based analysis, modeling, and learning from signal and data distributions.
CoRR, 2016

2015
Convergence of the k-Means Minimization Problem using Γ-Convergence.
SIAM J. Appl. Math., 2015

2014
Self-reinforced Meta Learning for Belief Generation.
Proceedings of the Research and Development in Intelligent Systems XXXI, 2014


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