Julie Bessac

Orcid: 0000-0001-6407-2423

According to our database1, Julie Bessac authored at least 15 papers between 2017 and 2023.

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

Timeline

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Links

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Bibliography

2023
Neural networks for parameter estimation in intractable models.
Comput. Stat. Data Anal., September, 2023

Black-box statistical prediction of lossy compression ratios for scientific data.
Int. J. High Perform. Comput. Appl., July, 2023

A Lightweight, Effective Compressibility Estimation Method for Error-bounded Lossy Compression.
Proceedings of the IEEE International Conference on Cluster Computing, 2023

2022
Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification.
CoRR, 2022

Understanding the Effects of Modern Compressors on the Community Earth Science Model.
Proceedings of the 8th IEEE/ACM International Workshop on Data Analysis and Reduction for Big Scientific Data, 2022

2021
Power grid frequency prediction using spatiotemporal modeling.
Stat. Anal. Data Min., 2021

Online data analysis and reduction: An important Co-design motif for extreme-scale computers.
Int. J. High Perform. Comput. Appl., 2021

Exploring Lossy Compressibility through Statistical Correlations of Scientific Datasets.
Proceedings of the 2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data, 2021

Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODE.
Proceedings of the Mathematical and Scientific Machine Learning, 2021

2020
Statistical Treatment of Inverse Problems Constrained by Differential Equations-Based Models with Stochastic Terms.
SIAM/ASA J. Uncertain. Quantification, 2020

SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors.
Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

Image Segmentation for Dust Detection Using Semi-supervised Machine Learning.
Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019
Global Sensitivity Analysis for Statistical Model Parameters.
SIAM/ASA J. Uncertain. Quantification, 2019

2018
Efficient computation of derivatives for solving optimization problems in R and Python using SWIG-generated interfaces to ADOL-C.
Optim. Methods Softw., 2018

2017
Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales.
Proceedings of the Euro-Par 2017: Parallel Processing - 23rd International Conference on Parallel and Distributed Computing, Santiago de Compostela, Spain, August 28, 2017


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