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Matthew Thorpe
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2020 – today
- 2024
- [j9]Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schönlieb, Matthew Thorpe, Konstantinos C. Zygalakis:
A linear transportation Lp distance for pattern recognition. Pattern Recognit. 147: 110080 (2024) - [j8]Adrien Weihs, Matthew Thorpe:
Consistency of Fractional Graph-Laplacian Regularization in Semisupervised Learning with Finite Labels. SIAM J. Math. Anal. 56(4): 4253-4295 (2024) - [i19]Xinran Liu, Rocio Diaz Martin, Yikun Bai, Ashkan Shahbazi, Matthew Thorpe, Akram Aldroubi, Soheil Kolouri:
Expected Sliced Transport Plans. CoRR abs/2410.12176 (2024) - 2023
- [j7]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:
Navigating the development challenges in creating complex data systems. Nat. Mac. Intell. 5(7): 681-686 (2023) - [c6]Yikun Bai, Bernhard Schmitzer, Matthew Thorpe, Soheil Kolouri:
Sliced Optimal Partial Transport. CVPR 2023: 13681-13690 - [i18]Xinran Liu, Yikun Bai, Huy Tran, Zhanqi Zhu, Matthew Thorpe, Soheil Kolouri:
PTLp: Partial Transport Lp Distances. CoRR abs/2307.13571 (2023) - [i17]Adrien Weihs, Jalal Fadili, Matthew Thorpe:
Discrete-to-Continuum Rates of Convergence for $p$-Laplacian Regularization. CoRR abs/2310.12691 (2023) - [i16]Keaton Hamm, Caroline Moosmüller, Bernhard Schmitzer, Matthew Thorpe:
Manifold learning in Wasserstein space. CoRR abs/2311.08549 (2023) - 2022
- [j6]Tianji Cai, Junyi Cheng, Bernhard Schmitzer, Matthew Thorpe:
The Linearized Hellinger-Kantorovich Distance. SIAM J. Imaging Sci. 15(1): 45-83 (2022) - [c5]Matthew Thorpe, Tan Minh Nguyen, Hedi Xia, Thomas Strohmer, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
GRAND++: Graph Neural Diffusion with A Source Term. ICLR 2022 - [i15]Irene Fonseca, Lisa Maria Kreusser, Carola-Bibiane Schönlieb, Matthew Thorpe:
Γ-Convergence of an Ambrosio-Tortorelli approximation scheme for image segmentation. CoRR abs/2202.04965 (2022) - [i14]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:
Classification of datasets with imputed missing values: does imputation quality matter? CoRR abs/2206.08478 (2022) - [i13]Nicolás García Trillos, Ryan Murray, Matthew Thorpe:
Rates of Convergence for Regression with the Graph Poly-Laplacian. CoRR abs/2209.02305 (2022) - [i12]Yikun Bai, Bernhard Schmitzer, Matthew Thorpe, Soheil Kolouri:
Sliced Optimal Partial Transport. CoRR abs/2212.08049 (2022) - 2021
- [j5]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:
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat. Mach. Intell. 3(3): 199-217 (2021) - [c4]Matthew Thorpe, Bao Wang:
Robust Certification for Laplace Learning on Geometric Graphs. MSML 2021: 896-920 - [i11]Matthew Thorpe, Bao Wang:
Robust Certification for Laplace Learning on Geometric Graphs. CoRR abs/2104.10837 (2021) - 2020
- [c3]Jeff Calder, Brendan Cook, Matthew Thorpe, Dejan Slepcev:
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates. ICML 2020: 1306-1316 - [i10]Nicolás García Trillos, Ryan Murray, Matthew Thorpe:
From graph cuts to isoperimetric inequalities: Convergence rates of Cheeger cuts on data clouds. CoRR abs/2004.09304 (2020) - [i9]Jeff Calder, Dejan Slepcev, Matthew Thorpe:
Rates of Convergence for Laplacian Semi-Supervised Learning with Low Labeling Rates. CoRR abs/2006.02765 (2020) - [i8]Jeff Calder, Brendan Cook, Matthew Thorpe, Dejan Slepcev:
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates. CoRR abs/2006.11184 (2020) - [i7]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:
Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: a systematic methodological review. CoRR abs/2008.06388 (2020) - [i6]Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schönlieb, Matthew Thorpe, Konstantinos C. Zygalakis:
A Linear Transportation Lp Distance for Pattern Recognition. CoRR abs/2009.11262 (2020)
2010 – 2019
- 2019
- [j4]Dejan Slepcev, Matthew Thorpe:
Analysis of p-Laplacian Regularization in Semisupervised Learning. SIAM J. Math. Anal. 51(3): 2085-2120 (2019) - [i5]Oliver M. Crook, Tim Hurst, Carola-Bibiane Schönlieb, Matthew Thorpe, Konstantinos C. Zygalakis:
PDE-Inspired Algorithms for Semi-Supervised Learning on Point Clouds. CoRR abs/1909.10221 (2019) - 2018
- [c2]Serim Park, Matthew Thorpe:
Representing and Learning High Dimensional Data With the Optimal Transport Map From a Probabilistic Viewpoint. CVPR 2018: 7864-7872 - [i4]Matthew M. Dunlop, Dejan Slepcev, Andrew M. Stuart, Matthew Thorpe:
Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithms. CoRR abs/1805.09450 (2018) - 2017
- [j3]Matthew Thorpe, Serim Park, Soheil Kolouri, Gustavo K. Rohde, Dejan Slepcev:
A Transportation Lp Distance for Signal Analysis. J. Math. Imaging Vis. 59(2): 187-210 (2017) - [j2]Soheil Kolouri, Se Rim Park, Matthew Thorpe, Dejan Slepcev, Gustavo K. Rohde:
Optimal Mass Transport: Signal processing and machine-learning applications. IEEE Signal Process. Mag. 34(4): 43-59 (2017) - [i3]Dejan Slepcev, Matthew Thorpe:
Analysis of $p$-Laplacian Regularization in Semi-Supervised Learning. CoRR abs/1707.06213 (2017) - 2016
- [i2]Soheil Kolouri, Serim Park, Matthew Thorpe, Dejan Slepcev, Gustavo K. Rohde:
Transport-based analysis, modeling, and learning from signal and data distributions. CoRR abs/1609.04767 (2016) - [i1]Matthew Thorpe, Serim Park, Soheil Kolouri, Gustavo K. Rohde, Dejan Slepcev:
A Transportation Lp Distance for Signal Analysis. CoRR abs/1609.08669 (2016) - 2015
- [j1]Matthew Thorpe, Florian Theil, Adam M. Johansen, Neil Cade:
Convergence of the k-Means Minimization Problem using Γ-Convergence. SIAM J. Appl. Math. 75(6): 2444-2474 (2015) - 2014
- [c1]Alexandros Gkiokas, Alexandra I. Cristea, Matthew Thorpe:
Self-reinforced Meta Learning for Belief Generation. SGAI Conf. 2014: 185-190
Coauthor Index
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last updated on 2024-11-25 23:38 CET by the dblp team
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