How do you create memorable poster for top tier conferences ( ICML/ICLR/NEURips ect…) [D]

· Source: Machine Learning · Field: Science & Research — Research Methodology & Innovation, Artificial Intelligence & Machine Learning · Depth: Intermediate, extended

Summary

A Reddit discussion and an embedded YouTube video address the challenge of creating effective academic posters for top-tier conferences like ICML, ICLR, and NeurIPS. The initial post highlights difficulties with design, theoretical content presentation, sizing choices, and the nearly $100 CAD printing cost. Responses emphasize maximizing poster size, limiting text and math in favor of plots and key takeaways, and engaging actively with attendees. The video critically analyzes traditional poster design, arguing it's inefficient for knowledge transfer and proposes a "billboard"-style approach. This new design prioritizes a large, plain-language main finding, supplemented by an "ammo bar" for detailed answers, a "silent presenter bar" for overview, and a QR code linking to the full paper, aiming for easier creation and better attendee engagement.

Key takeaway

For AI Scientists and Research Scientists preparing for conference poster sessions, you should adopt a "billboard" design philosophy. Focus on presenting your single, most important finding in large, plain language at the center of your poster. Supplement this with an "ammo bar" for technical details and a QR code for your full paper, ensuring your poster serves as a hook rather than a dense document, maximizing engagement and knowledge transfer.

Key insights

Effective academic posters prioritize clear, concise communication of core findings over dense, detailed text.

Principles

Method

Lead with a large, plain-language main finding. Include an "ammo bar" for detailed answers, a "silent presenter bar" for overview, and a QR code for the full paper, enabling progressive disclosure.

In practice

Topics

Best for: AI Scientist, Research Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.