Research map / Optimization / Distributed and compressed
LEGACY: A Lightweight Dynamic Gradient Compression Strategy for Distributed Deep Learning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training (NeurIPS 2020 · similarity 0.90)
- On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning (AAAI 2020 · similarity 0.90)
- Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism (NeurIPS 2025 · similarity 0.87)
- Sketched Adaptive Distributed Deep Learning: A Sharp Convergence Analysis (NeurIPS 2025 · similarity 0.87)
- From Promise to Practice: Realizing High-performance Decentralized Training (ICLR 2025 · similarity 0.86)
- Auto-Compressing Networks (NeurIPS 2025 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.