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Leveraging State-of-the-Art Engines for Large-Scale Data Analysis in High Energy Physics., , , , and . J. Grid Comput., 21 (1): 9 (March 2023)Scalable unsupervised learning for deep discrete generative models: novel variational algorithms and their software realizations.. University of Oldenburg, Germany, (2020)Declarative Big Data Analysis for High-Energy Physics: TOTEM Use Case., , , , , , , , , and 7 other author(s). Euro-Par, volume 11725 of Lecture Notes in Computer Science, page 241-255. Springer, (2019)Big Data Tools and Cloud Services for High Energy Physics Analysis in TOTEM Experiment., , , , , , , , , and 7 other author(s). UCC Companion, page 5-6. IEEE, (2018)Prototyping a ROOT-based distributed analysis workflow for HL-LHC: The CMS use case., , , , , , , and . Comput. Phys. Commun., (2024)Evolutionary Variational Optimization of Generative Models., , and . CoRR, (2020)Direct Evolutionary Optimization of Variational Autoencoders With Binary Latents., , and . CoRR, (2020)Inference and Learning in a Latent Variable Model for Beta Distributed Interval Data., , , , and . Entropy, 23 (5): 552 (2021)Evolutionary Variational Optimization of Generative Models., , and . J. Mach. Learn. Res., (2022)Prototyping a ROOT-based distributed analysis workflow for HL-LHC: the CMS use case., , , , , , , and . CoRR, (2023)