A lightweight workflow for keeping up with AI conference papers
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If you follow ML research, you know the feeling: ICLR, NeurIPS, ICML and CVPR each drop thousands of papers, and your "to-read" list quietly turns into a graveyard. Here's the workflow that finally worked for me. It's less about reading more and more about deciding faster. 1. Go topic-first, not conference-first Browsing an entire conference proceedings front to back is how you burn out. Instead I start from a topic and pull the relevant papers across conferences and years. Lately I've been…
1Key Takeaways
- If you follow ML research, you know the feeling: ICLR, NeurIPS, ICML and CVPR each drop thousands of papers, and your "to-read" list quietly turns into a graveyard.
- Here's the workflow that finally worked for me.
- It's less about reading more and more about deciding faster.
- Go topic-first, not conference-first Browsing an entire conference proceedings front to back is how you burn out.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that if you follow ML research, you know the feeling: ICLR, NeurIPS, ICML and CVPR each drop thousands of papers, and your "to-read" list quietly turns into a graveyard.
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