English(EN) We turned 10 years of ML papers into one page per topic: benchmark records, what got tried, what reviewers expect [P]

机器学习研究地图：10年论文按主题整理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-03 05:09

Latentic Labs 创建了一个研究地图，将来自 ICML 和 NeurIPS 等主要会议的10年机器学习论文整理成大约1260个主题。该平台从数万篇论文中提取基准记录和消融研究，并将每个报告的数字与其来源链接起来。虽然由于潜在的可比性问题和持续更新，它并非一个确定的排行榜，但该工具旨在为理解研究趋势提供一个起点，包括特定主题内最佳报告数字的演变和常用基线。 AI

影响为研究人员提供机器学习研究趋势和基准进展的结构化概览。

排序理由该集群描述了一个组织和分析学术研究论文的新工具，属于研究类别。

在 r/MachineLearning 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 3 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了一个组织和分析学术研究论文的新工具，属于研究类别。
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

r/MachineLearning TIER_1 English(EN) · /u/latenticlabs · 2026-11-03 05:09

We turned 10 years of ML papers into one page per topic: benchmark records, what got tried, what reviewers expect [P]

<div class="md"><p>We've been working on this for a while and shipped it today.</p> <p>We took every accepted paper from ICML, NeurIPS, ICLR, CVPR and AAAI since 2016 (about 109k), grouped them into ~1,260 topics, and pulled the results tables and ablations out of …

本文如何被排名

报道来源 [1]

We turned 10 years of ML papers into one page per topic: benchmark records, what got tried, what reviewers expect [P]

相关实体

相关话题