{"total":30,"rows":[{"id":"2210.12538","title":"Compressing multidimensional weather and climate data into neural networks","created":"2022-10-22","cited_by_count":31,"area":null},{"id":"2409.11585","title":"Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework","created":"2024-09-17","cited_by_count":19,"area":null},{"id":"2311.12831","title":"ECNR: Efficient Compressive Neural Representation of Time-Varying Volumetric Datasets","created":"2023-10-02","cited_by_count":17,"area":null},{"id":"2308.02494","title":"Adaptively Placed Multi-Grid Scene Representation Networks for Large-Scale Data Visualization","created":"2023-07-16","cited_by_count":16,"area":null},{"id":"2503.01505","title":"Lossy Neural Compression for Geospatial Analytics: A Review","created":"2025-03-03","cited_by_count":10,"area":null},{"id":"2304.10516","title":"Distributed Neural Representation for Reactive in situ Visualization","created":"2023-03-28","cited_by_count":8,"area":null},{"id":"2309.04037","title":"SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks","created":"2023-09-07","cited_by_count":6,"area":null},{"id":"2407.04272","title":"Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression","created":"2024-07-05","cited_by_count":5,"area":null},{"id":"2409.05357","title":"Attention Based Machine Learning Methods for Data Reduction with Guaranteed Error Bounds","created":"2024-09-09","cited_by_count":5,"area":null},{"id":"2507.02494","title":"MC-INR: Efficient Encoding of Multivariate Scientific Simulation Data using Meta-Learning and Clustered Implicit Neural Representations","created":"2025-07-03","cited_by_count":4,"area":null},{"id":"2402.05965","title":"Hybrid Neural Representations for Spherical Data","created":"2024-02-05","cited_by_count":3,"area":null},{"id":"2409.05785","title":"NeurLZ: An Online Neural Learning-Based Method to Enhance Scientific Lossy Compression","created":"2024-09-09","cited_by_count":3,"area":null},{"id":"2503.06623","title":"Transforming Weather Data from Pixel to Latent Space","created":"2025-03-09","cited_by_count":3,"area":null},{"id":"2507.03836","title":"F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding","created":"2025-07-04","cited_by_count":3,"area":null},{"id":"2404.18063","title":"Machine Learning Techniques for Data Reduction of CFD Applications","created":"2024-04-28","cited_by_count":2,"area":null},{"id":"2412.17184","title":"Foundation Model for Lossy Compression of Spatiotemporal Scientific Data","created":"2024-12-22","cited_by_count":2,"area":null},{"id":"2604.09543","title":"ANTIC: Adaptive Neural Temporal In-situ Compressor","created":"2026-04-10","cited_by_count":2,"area":null},{"id":"2606.05389","title":"Residual Modeling for High-Fidelity Learned Compression of Scientific Data","created":"2026-06-03","cited_by_count":2,"area":null},{"id":"2407.15037","title":"Lessons Learned on the Path to Guaranteeing the Error Bound in Lossy Quantizers","created":"2024-07-21","cited_by_count":0,"area":null},{"id":"2409.18295","title":"Enhancing Lossy Compression Through Cross-Field Information for Scientific Applications","created":"2024-09-26","cited_by_count":0,"area":null},{"id":"2505.06316","title":"GraphComp: Extreme Error-bounded Compression of Scientific Data via Temporal Graph Autoencoders","created":"2025-05-08","cited_by_count":0,"area":null},{"id":"2510.23632","title":"LLMComp: A Language Modeling Paradigm for Error-Bounded Scientific Data Compression (Technical Report)","created":"2025-10-24","cited_by_count":0,"area":null},{"id":"2511.02659","title":"In Situ Training of Implicit Neural Compressors for Scientific Simulations via Sketch-Based Regularization","created":"2025-11-04","cited_by_count":0,"area":null},{"id":"2512.21433","title":"DeepCQ: General-Purpose Deep-Surrogate Framework for Lossy Compression Quality Prediction","created":"2025-12-24","cited_by_count":0,"area":null},{"id":"2602.00079","title":"Embedding Compression via Spherical Coordinates","created":"2026-01-22","cited_by_count":0,"area":null},{"id":"2602.20097","title":"Mitigating Artifacts in Pre-quantization Based Scientific Data Compressors with Quantization-aware Interpolation","created":"2026-02-23","cited_by_count":0,"area":null},{"id":"2607.18187","title":"EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Database","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2609.06008","title":"Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model","created":"2026-09-05","cited_by_count":0,"area":null},{"id":"2609.23185","title":"Neural Residual Modeling for Scientific Data Compression under Guaranteed Error Bounds","created":"2026-09-19","cited_by_count":0,"area":null},{"id":"2610.06095","title":"Transmission Factors for Lossy Compression of PDE Training Data: Measuring What Reaches a Trained Operator","created":"2026-10-05","cited_by_count":0,"area":null}],"next_cursor":null}