Towards the Systematic Reporting of the Energy and Carbon Footprints of\n Machine Learning

  • 类型：arxiv
  • 标识：2002.05651
  • 链接：https://arxiv.org/abs/2002.05651
  • 主题：agent
  • 主分类：evaluation
  • 形态：position
  • 被引：754
  • 被引来源：Semantic Scholar
  • S2被引：754
  • OpenAlex被引：308
  • 影响力被引：64
  • TLDR：A framework is introduced that makes accounting easier by providing a simple interface for tracking realtime energy consumption and carbon emissions, as well as generating standardized online appendices, and creates a leaderboard for energy efficient reinforcement learning algorithms to incentivize responsible research.
  • OpenAlex ID：W4310492983
  • OpenAlex DOI：10.48550/arxiv.2002.05651
  • DOI：10.48550/arxiv.2002.05651
  • DOI来源：OpenAlex
  • 开放获取：green
  • 开放获取链接：https://arxiv.org/pdf/2002.05651
  • OpenAlex更新：2026-08-21
  • 待LLM分类：否
  • 成熟度：research
  • 场景：energy-tracking、ml-sustainability
  • 标题中文：迈向机器学习能耗与碳足迹的系统化报告
  • TLDR中文：引入了一个框架，通过提供简洁接口来跟踪实时能耗与碳排放、生成标准化的在线附录来简化核算，并为节能的强化学习算法建立排行榜以激励负责任的研究
  • 来源文件：
  • [OpenAlex discover]
  • [S2 enrich]