Matthew Thorpe

1. Optimal Mass Transport: Signal processing and machine-learning applications
   2017/07/01 by Soheil Kolouri, Se Rim Park, Matthew Thorpe +3 · 17 citations
   Computer Science · Physics and Astronomy · #Medical Image Segmentation Techniques #Topological and Geometric Data Analysis #Statistical Mechanics and Entropy
2. Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label\n Rates
   2020/06/19 by Jeff Calder, Calder, Jeff, Brendan Cook +5 · 5 citations
   Computer Science · #05C81 #35J08 #35J15 #35R02 #68T05 #Analysis of PDEs (math.AP) #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #FOS: Mathematics #G.1.8 #G.2.2 #I.2.6 #I.4.0 #I.5.3 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Multimodal Machine Learning Applications #Numerical Analysis (math.NA) #Text and Document Classification Technologies
3. Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease
   2024/04/01 by Felicity Liew, Claudia Efstathiou, Sara Fontanella +97 · 1 voice · 5 citations
   Medicine · Neuroscience · #Long-Term Effects of COVID-19 #Neuroinflammation and Neurodegeneration Mechanisms #COVID-19 Clinical Research Studies
4. Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning\n Algorithms
   2018/05/23 by Matthew M. Dunlop, Dejan Slepčev, Dunlop, Matthew M. +5 · 2 citations
   Decision Sciences · Engineering · Mathematics · #49J55 #62C10 #62F15 #62G20 #Advanced Bandit Algorithms Research #Advanced Optimization Algorithms Research #Analysis of PDEs (math.AP) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques
5. Rates of Convergence for Laplacian Semi-Supervised Learning with Low Labeling Rates
   2020/06/04 by Jeff Calder, Dejan Slepčev, Calder, Jeff +3 · 2 citations
   Computer Science · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Statistical Methods and Inference #Statistics Theory (math.ST) #Stochastic Gradient Optimization Techniques
6. Asymptotic analysis of the Ginzburg–Landau functional on point clouds
   2018/12/27 by Matthew Thorpe, Florian Theil · 1 citation
7. Manifold learning in Wasserstein space
   2023/11/14 by Keaton Hamm, Hamm, Keaton, Caroline Moosmüller +5 · 2 citations
   Computer Science · #41A65 #49Q22 #53Z50 #58B20 #Differential Geometry (math.DG) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Topological and Geometric Data Analysis
8. Expected Sliced Transport Plans
   2024/10/16 by Xinran Liu, Liu, Xinran, Rocío Díaz Martín +11 · 3 citations
   Business, Management and Accounting · Engineering · #Advanced Manufacturing and Logistics Optimization #FOS: Computer and information sciences #FOS: Mathematics #Law, logistics, and international trade #Machine Learning (cs.LG) #Metric Geometry (math.MG)
9. From graph cuts to isoperimetric inequalities: Convergence rates of Cheeger cuts on data clouds
   2020/04/20 by Nicolás García Trillos, Trillos, Nicolas Garcia, Ryan Murray +3 · 1 citation
   Computer Science · Engineering · Mathematics · #Topological and Geometric Data Analysis #3D Shape Modeling and Analysis #Point processes and geometric inequalities
10. Large data limit for a phase transition model with the p-Laplacian on point clouds
    2018/02/23 by Riccardo Cristoferi, Matthew Thorpe, Cristoferi, Riccardo +1 · 1 citation
    Computer Science · Mathematics · #Analysis of PDEs (math.AP) #FOS: Mathematics #Geometric Analysis and Curvature Flows #Stochastic processes and statistical mechanics #Topological and Geometric Data Analysis
11. Γ-Convergence of an Ambrosio-Tortorelli approximation scheme for image segmentation
    2022/02/10 by Irene Fonseca, Fonseca, Irene, Lisa Maria Kreußer +5 · 1 citation
    Engineering · Mathematics · #Analysis of PDEs (math.AP) #FOS: Mathematics #Mathematical Approximation and Integration #Numerical Analysis (math.NA) #Numerical methods in inverse problems #Optimization and Control (math.OC) #Thermoelastic and Magnetoelastic Phenomena