Текущий год

Том 6, 2025 / Выпуск 4

DOI: 10.1615/JMachLearnModelComput.v6.i4

Table of Contents:

CONDITIONAL PSEUDO-REVERSIBLE NORMALIZING FLOW FOR SURROGATE MODELING IN QUANTIFYING UNCERTAINTY PROPAGATION Minglei Yang, Pengjun Wang, Ming Fan, Dan Lu, Yanzhao Cao, Guannan Zhang

Minglei Yang
Oak Ridge National Laboratory

Pengjun Wang
Department of Mathematics and Statistics, Auburn University, Auburn, AL 36849, USA

Ming Fan
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, USA

Dan Lu
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, USA

Yanzhao Cao
Department of Mathematics and Statistics, Auburn University, Auburn, AL 36849; School of Mathematics, Sun Yat Sun University, China

Guannan Zhang
Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

1-28 страниц
DOI: 10.1615/JMachLearnModelComput.2025060260

DIMENSION-REDUCED RECONSTRUCTION MAP LEARNING FOR PARAMETER ESTIMATION IN LIKELIHOOD-FREE INFERENCE PROBLEMS Rui Zhang, Oksana Chkrebtii, Dongbin Xiu

Rui Zhang
Department of Statistics, The Ohio State University, Columbus, Ohio, USA

Oksana Chkrebtii
The Ohio State University

Dongbin Xiu
Department of Mathematics, The Ohio State University, Columbus, 43210 Ohio, USA

29-50 страниц
DOI: 10.1615/JMachLearnModelComput.2025060234

SPARSE L¹-AUTOENCODERS FOR SCIENTIFIC DATA COMPRESSION Matthias Chung, Richard Archibald, Paul Atzberger, Jack Michael Solomon

Matthias Chung
Emory University

Richard Archibald
Department of Mathematics, Florida State University, Tallahassee, Florida, USA

Paul Atzberger
Department of Mathematics, University of California Santa Barbara, Santa Barbara, CA 93106, USA

Jack Michael Solomon
Department of Mathematics, Emory University, 400 Dowman Drive, Atlanta, GA, USA

51-71 страниц
DOI: 10.1615/JMachLearnModelComput.2025058608

NUMERICAL SPLITTING SCHEMES AS THE CORNERSTONE FOR MINI-BATCH OPTIMIZATION: ON THE IMPORTANCE OF WELL-BALANCED METHODS Bilel Bensaid, Gaël Poëtte, Rodolphe Turpault

Bilel Bensaid
CEA, CESTA, DAM, 15 av. des Sablières, Le Barp, 33114, France; Institut de Mathématiques de Bordeaux, Université de Bordeaux, CNRS, Bordeaux INP, France

Gaël Poëtte
CEA, CESTA, DAM, 15 av. des Sablières, Le Barp, 33114, France; Institut de Mathématiques de Bordeaux, Université de Bordeaux, CNRS, Bordeaux INP, France

Rodolphe Turpault
Institut de Mathématiques de Bordeaux, Université de Bordeaux, CNRS, Bordeaux INP, France

73-124 страниц
DOI: 10.1615/JMachLearnModelComput.2025059453

FEDERATED LEARNING ON STOCHASTIC NEURAL NETWORKS Jingqiao Tang, Ryan Bausback, Feng Bao, Richard Archibald

Jingqiao Tang
Florida State University

Ryan Bausback
Department of Mathematics at Florida State University, Tallahassee, Florida 32304, USA

Feng Bao
Department of Mathematics, Florida State University, Florida 32304, USA

Richard Archibald
Division of Computer Science and Mathematics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, USA

125-150 страниц
DOI: 10.1615/JMachLearnModelComput.2025060047

INDEX, VOLUME 6, 2025 151-155 страниц
DOI: 10.1615/JMachLearnModelComput.v6.i4.60

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