Petrus H. Zwart

According to our database, Petrus H. Zwart authored at least 13 papers between 2019 and 2025.

Collaborative distances:

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

2025

Probabilistic Conformal Coverage Guarantees in Small-Data Settings.

Petrus H. Zwart

CoRR, September, 2025

Optimizing inference of segmentation on high-resolution images in MLExchange.

Shizhao Lu, Tanny Chavez, Wiebke Koepp, Guanhua Hao, Petrus H. Zwart, Alexander Hexemer

J. Supercomput., June, 2025

Behind the Noise: Conformal Quantile Regression Reveals Emergent Representations.

Petrus H. Zwart, Tamás Varga, Odeta Qafoku, James A. Sethian

CoRR, May, 2025

2024

qlty: Handling large tensors in scientific imaging deep-learning workflows.

Petrus H. Zwart

Softw. Impacts, 2024

2023

Image Segmentation using U-Net Architecture for Powder X-ray Diffraction Images.

Howard Yanxon, Eric J. Roberts, Hannah Parraga, James Weng, Wenqian Xu, Uta Ruett, Alexander Hexemer, Petrus H. Zwart, Nicholas Schwarz

CoRR, 2023

DLSIA: Deep Learning for Scientific Image Analysis.

Eric J. Roberts, Tanny Chavez, Alexander Hexemer, Petrus H. Zwart

CoRR, 2023

Deploying machine learning based segmentation for scientific imaging analysis at synchrotron facilities.

Guanhua Hao, Eric J. Roberts, Tanny Chavez, Zhuowen Zhao, Elizabeth Holman, Howard Yanxon, Adam Green, Harinarayan Krishnan, Daniela Ushizima, Dylan McReynolds, Nicholas Schwarz, Petrus H. Zwart, Alexander Hexemer, Dilworth Parkinson

Proceedings of the Image Processing: Algorithms and Systems XXI, 2023

2022

MLExchange: A web-based platform enabling exchangeable machine learning workflows.

Zhuowen Zhao, Tanny Chavez, Elizabeth Holman, Guanhua Hao, Adam Green, Harinarayan Krishnan, Dylan McReynolds, Ronald Pandolfi, Eric J. Roberts, Petrus H. Zwart, Howard Yanxon, Nicholas Schwarz, Subramanian Sankaranarayanan, Sergei V. Kalinin, Apurva Mehta, Stuart Campbell, Alexander Hexemer

CoRR, 2022

MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies.

Zhuowen Zhao, Tanny Chavez, Elizabeth Holman, Guanhua Hao, Adam Green, Harinarayan Krishnan, Dylan McReynolds, Ronald Pandolfi, Eric J. Roberts, Petrus H. Zwart, Howard Yanxon, Nicholas Schwarz, Subramanian Sankaranarayanan, Sergei V. Kalinin, Apurva Mehta, Stuart Campbell, Alexander Hexemer

Proceedings of the 4th Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing, 2022

2021

Learning from learning machines: a new generation of AI technology to meet the needs of science.

Luca Pion-Tonachini, Kristofer E. Bouchard, Héctor García Martín, Sean Peisert, W. Bradley Holtz, Anil Aswani, Dipankar Dwivedi, Haruko M. Wainwright, Ghanshyam Pilania, Benjamin Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel B. Arnold, Alejandro Wolf-Yadlin, Sarah Powers, Sharlee Climer, Quinn Jackson, Ty Carlson, Michael Sohn, Petrus H. Zwart, Neeraj Kumar, Amy Justice, Claire J. Tomlin, Daniel A. Jacobson, Gos Micklem, Georgios V. Gkoutos, Peter J. Bickel, Jean-Baptiste Cazier, Juliane Müller, Bobbie-Jo Webb-Robertson, Rick Stevens, Mark Anderson, Kenneth Kreutz-Delgado, Michael W. Mahoney, James B. Brown

CoRR, 2021

High-Performance Hybrid-Global-Deflated-Local Optimization with Applications to Active Learning.

Marcus Michael Noack, David Perryman, Harinarayan Krishnan, Petrus H. Zwart

Proceedings of the 3rd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing, 2021

2020

K-means-driven Gaussian Process data collection for angle-resolved photoemission spectroscopy.

Charles N. Melton, Marcus Michael Noack, Taisuke Ohta, Thomas E. Beechem, Jeremy Robinson, Xiaotian Zhang, Aaron Bostwick, Chris Jozwiak, Roland J. Koch, Petrus H. Zwart, Alexander Hexemer, Eli Rotenberg

Mach. Learn. Sci. Technol., 2020

2019

Computational Strategies to Increase Efficiency of Gaussian-Process-Driven Autonomous Experiments.

Marcus Michael Noack, Petrus H. Zwart

Proceedings of the 1st IEEE/ACM Annual Workshop on Large-scale Experiment-in-the-Loop Computing, 2019