arXiv Machine Learning August 14, 2026 By Sophia N. Wilson, Jens Hesselbjerg Christensen, Raghavendra Selvan

Performance-Carbon Trade-Offs across Architectural Biases in Shear Flow Forecasting

Read the original on arXiv Machine Learning →

arXiv:2509. 24517v3 Announce Type: replace Abstract: Development of modern deep learning methods has been driven primarily by the push for improving model efficacy (accuracy metrics), leading to large-scale models that require massive computational resources and result in considerable carbon footprint across the model lifecycle.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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Estimating carbon pools in the European Shelf sea environment: replacing reanalysis by model-informed machine learning?

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arXiv AI

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Seeking SOTA: Time-Series Forecasting Must Adopt Taxonomy-Specific Evaluation to Dispel Illusory Gains

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arXiv Machine Learning

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Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecasting

arXiv:2512. 22702v2 Announce Type: replace Abstract: Deep learning models have grown popular in time series applications.

By Valentina Moretti, Ivan Marisca, Cesare Alippi, Andrea Cini

benchmarks

arXiv Machine Learning

Jun 15

Can Deep Neural Networks Improve Compression of Very Large Scientific Data?

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arXiv Machine Learning

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Nipping the Butterfly Effect in the Bud: Self-Output Fine-Tuning for Autoregressive Weather Prediction

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By Yun-Ye Cai, Hsuan-Tien Lin

fine-tuning benchmarks

arXiv Machine Learning

Aug 10

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction

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