Matthew Thorpe

According to our database, Matthew Thorpe authored at least 30 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A linear transportation Lp distance for pattern recognition.

Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schönlieb, Matthew Thorpe, Konstantinos C. Zygalakis

Pattern Recognit., March, 2024

Consistency of Fractional Graph-Laplacian Regularization in Semisupervised Learning with Finite Labels.

Adrien Weihs, Matthew Thorpe

SIAM J. Math. Anal., 2024

Expected Sliced Transport Plans.

Xinran Liu, Rocio Diaz Martin, Yikun Bai, Ashkan Shahbazi, Matthew Thorpe, Akram Aldroubi, Soheil Kolouri

CoRR, 2024

2023

Navigating the development challenges in creating complex data systems.

Sören Dittmer, Michael Roberts, Julian D. Gilbey, Ander Biguri, Ian Selby, Anna Breger, Matthew Thorpe, Jonathan R. Weir-McCall, Effrossyni Gkrania-Klotsas, Anna Korhonen, Emily R. Jefferson, Georg Langs, Guang Yang, Helmut Prosch, Jan Stanczuk, Jing Tang, Judith Babar, Lorena Escudero Sanchez, Philip Teare, Mishal Patel, Marcel Wassin, Markus Holzer, Nicholas Walton, Pietro Lió, Tolou Shadbahr, Evis Sala, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb

Nat. Mac. Intell., July, 2023

Manifold learning in Wasserstein space.

Keaton Hamm, Caroline Moosmüller, Bernhard Schmitzer, Matthew Thorpe

CoRR, 2023

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

Adrien Weihs, Jalal Fadili, Matthew Thorpe

CoRR, 2023

PTL^p: Partial Transport L^p Distances.

Xinran Liu, Yikun Bai, Huy Tran, Zhanqi Zhu, Matthew Thorpe, Soheil Kolouri

CoRR, 2023

Sliced Optimal Partial Transport.

Yikun Bai, Bernhard Schmitzer, Matthew Thorpe, Soheil Kolouri

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

The Linearized Hellinger-Kantorovich Distance.

Tianji Cai, Junyi Cheng, Bernhard Schmitzer, Matthew Thorpe

SIAM J. Imaging Sci., 2022

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

Nicolás García Trillos, Ryan Murray, Matthew Thorpe

CoRR, 2022

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

Tolou Shadbahr, Michael Roberts, Jan Stanczuk, Julian D. Gilbey, Philip Teare, Sören Dittmer, Matthew Thorpe, Ramón Viñas Torné, Evis Sala, Pietro Lió, Mishal Patel, AIX-COVNET Collaboration, James H. F. Rudd, Tuomas Mirtti, Antti Rannikko, John A. D. Aston, Jing Tang, Carola-Bibiane Schönlieb

CoRR, 2022

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

Irene Fonseca, Lisa Maria Kreusser, Carola-Bibiane Schönlieb, Matthew Thorpe

CoRR, 2022

GRAND++: Graph Neural Diffusion with A Source Term.

Matthew Thorpe, Tan Minh Nguyen, Hedi Xia, Thomas Strohmer, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang

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.

Michael Roberts, Derek Driggs, Matthew Thorpe, Julian D. Gilbey, Michael Yeung, Stephan Ursprung, Angelica I. Avilés-Rivero, Christian Etmann, Cathal McCague, Lucian Beer, Jonathan R. Weir-McCall, Zhongzhao Teng, Effrossyni Gkrania-Klotsas, James H. F. Rudd, Evis Sala, Carola-Bibiane Schönlieb

Nat. Mach. Intell., 2021

Robust Certification for Laplace Learning on Geometric Graphs.

Matthew Thorpe, Bao Wang

Proceedings of the Mathematical and Scientific Machine Learning, 2021

2020

A Linear Transportation L^p Distance for Pattern Recognition.

Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schönlieb, Matthew Thorpe, Konstantinos C. Zygalakis

CoRR, 2020

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

Michael Roberts, Derek Driggs, Matthew Thorpe, Julian D. Gilbey, Michael Yeung, Stephan Ursprung, Angelica I. Avilés-Rivero, Christian Etmann, Cathal McCague, Lucian Beer, Jonathan R. Weir-McCall, Zhongzhao Teng, James H. F. Rudd, Evis Sala, Carola-Bibiane Schönlieb

CoRR, 2020

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

Jeff Calder, Dejan Slepcev, Matthew Thorpe

CoRR, 2020

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

Nicolás García Trillos, Ryan Murray, Matthew Thorpe

CoRR, 2020

Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates.

Jeff Calder, Brendan Cook, Matthew Thorpe, Dejan Slepcev

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Analysis of p-Laplacian Regularization in Semisupervised Learning.

Dejan Slepcev, Matthew Thorpe

SIAM J. Math. Anal., 2019

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

Oliver M. Crook, Tim Hurst, Carola-Bibiane Schönlieb, Matthew Thorpe, Konstantinos C. Zygalakis

CoRR, 2019

2018

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

Matthew M. Dunlop, Dejan Slepcev, Andrew M. Stuart, Matthew Thorpe

CoRR, 2018

Representing and Learning High Dimensional Data With the Optimal Transport Map From a Probabilistic Viewpoint.

Serim Park, Matthew Thorpe

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017

Optimal Mass Transport: Signal processing and machine-learning applications.

Soheil Kolouri, Se Rim Park, Matthew Thorpe, Dejan Slepcev, Gustavo K. Rohde

IEEE Signal Process. Mag., 2017

A Transportation L^p Distance for Signal Analysis.

Matthew Thorpe, Serim Park, Soheil Kolouri, Gustavo K. Rohde, Dejan Slepcev

J. Math. Imaging Vis., 2017

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

Dejan Slepcev, Matthew Thorpe

CoRR, 2017

2016

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

Soheil Kolouri, Serim Park, Matthew Thorpe, Dejan Slepcev, Gustavo K. Rohde

CoRR, 2016

2015

Convergence of the k-Means Minimization Problem using Γ-Convergence.

Matthew Thorpe, Florian Theil, Adam M. Johansen, Neil Cade

SIAM J. Appl. Math., 2015

2014

Self-reinforced Meta Learning for Belief Generation.

Alexandros Gkiokas, Alexandra I. Cristea, Matthew Thorpe

Proceedings of the Research and Development in Intelligent Systems XXXI, 2014