Papers

• From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps (2026)
  Ghjulia Sialelli et al.
  4.39
• Right-sizing Recommendations (RSR): Cloud Workload Conformal Prediction for Virtual Machines in Data Center Operations (2026)
  Mehryar Majd et al.
  2.00
• Data Quality Profiling at Scale with Progressive Sampling: A Benchmark for Data-Centric AI Pipelines (2026)
  Laure Berti-Equille
  2.00
• FLUXtrapolation: A benchmark on extrapolating ecosystem fluxes (2026)
  Anya Fries et al.
  1.89
• Operational evaluation of data-driven forest fire forecasting models (2026)
  Shahbaz Alvi et al.
  1.78
• The future of AI in critical mineral exploration (2025)
  Jef Caers
  1.61
• Restless Multi-Process Multi-Armed Bandits with Applications to Self-Driving Microscopies (2025)
  Jaume Anguera Peris et al.
  1.61
• Incorporating LLM Embeddings for Variation Across the Human Genome (2025)
  Hongqian Niu et al.
  1.44
• Data-Driven Soil Organic Carbon Sampling: Integrating Spectral Clustering with Conditioned Latin Hypercube Optimization (2025)
  Weiying Zhao et al.
  1.28
• Using large language models to produce literature reviews: Usages and systematic biases of microphysics parametrizations in 2699 publications (2025)
  Tianhang Zhang et al.
  1.11
• SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era (2025)
  Elizaveta Semenova et al.
  1.06
• Verification and Validation for Trustworthy Scientific Machine Learning (2025)
  John D. Jakeman et al.
  1.06
• Applications of physics-informed scientific machine learning in subsurface science: A survey (2021)
  Alexander Y. Sun et al.
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• Bayesian tensor factorization for predicting clinical outcomes using integrated human genetics evidence (2022)
  Onuralp Soylemez
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• Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series (2022)
  Jose M. G. Vilar and Leonor Saiz
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• A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows (2023)
  Line Pouchard et al.
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• Ab initio uncertainty quantification in scattering analysis of microscopy (2023)
  Mengyang Gu et al.
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• Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics (2023)
  Chen Zhao et al.
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• Spinal Muscle Atrophy Disease Modelling as Bayesian Network (2023)
  Mohammed Ezzat Helal et al.
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• Optimized Dynamic Mode Decomposition for Reconstruction and Forecasting of Atmospheric Chemistry Data (2024)
  Meghana Velegar et al.
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• Simulation-Free Determination of Microstructure Representative Volume Element Size via Fisher Scores (2024)
  Wei Liu et al.
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• Automating the Discovery of Partial Differential Equations in Dynamical Systems (2024)
  Weizhen Li and Rui Carvalho
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• Revisiting the Efficacy of Signal Decomposition in AI-based Time Series Prediction (2024)
  Kexin Jiang et al.
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• Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis (2024)
  Geremy Loacham\'in Suntaxi et al.
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