{"total":14,"rows":[{"id":"2210.06640","title":"Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities","created":"2022-10-13","cited_by_count":38,"area":null},{"id":"2210.12538","title":"Compressing multidimensional weather and climate data into neural networks","created":"2022-10-22","cited_by_count":31,"area":null},{"id":"2405.03376","title":"CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer","created":"2024-05-06","cited_by_count":22,"area":null},{"id":"2111.04131","title":"Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines","created":"2021-11-07","cited_by_count":16,"area":null},{"id":"2406.13768","title":"FastPersist: Accelerating Model Checkpointing in Deep Learning","created":"2024-06-19","cited_by_count":6,"area":null},{"id":"2309.16743","title":"High Throughput Training of Deep Surrogates from Large Ensemble Runs","created":"2023-09-28","cited_by_count":3,"area":null},{"id":"2312.02368","title":"RINAS: Training with Dataset Shuffling Can Be General and Fast","created":"2023-12-04","cited_by_count":3,"area":null},{"id":"2404.10386","title":"I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey","created":"2024-04-16","cited_by_count":3,"area":null},{"id":"2310.10102","title":"KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training","created":"2023-10-16","cited_by_count":1,"area":null},{"id":"2211.00224","title":"SOLAR: A Highly Optimized Data Loading Framework for Distributed Training of CNN-based Scientific Surrogates","created":"2022-11-01","cited_by_count":0,"area":null},{"id":"2309.12576","title":"Understanding Patterns of Deep Learning ModelEvolution in Network Architecture Search","created":"2023-09-22","cited_by_count":0,"area":null},{"id":"2509.05207","title":"RapidGNN: Energy and Communication-Efficient Distributed Training on Large-Scale Graph Neural Networks","created":"2025-09-05","cited_by_count":0,"area":null},{"id":"2511.13724","title":"Preparation Meets Opportunity: Enhancing Data Preprocessing for ML Training With Seneca","created":"2025-09-24","cited_by_count":0,"area":null},{"id":"2609.24205","title":"A principled approach for energy-efficient training via phase-aware GPU frequency tuning","created":"2026-09-21","cited_by_count":0,"area":null}],"next_cursor":null}