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esp32-ai
A 28.9 million parameter language model runs on an $8 ESP32-S3 microcontroller entirely on-device, generating simple stories at about 9.5 tokens per second.
AI repository intelligence
FR-AI / ANALYSISWhy it is worth attention
It provides an interactive, visual way to navigate a large conference proceedings – combining UMAP-based 3D embedding, semantic search, and per-paper summaries – which is rare for a single conference dataset.
Who it is for
- ICML attendees and researchers
- ML paper readers seeking topical exploration
- Conference organizers and program chairs
- Data journalists covering AI research trends
Use cases
- Quickly finding papers similar to a known work via 3D map or semantic search
- Analyzing institutional affiliation trends across ICML 2026
- Generating custom paper lists with bookmarks and CSV export for review
- Filtering papers by conference day for scheduling or attendance planning
Strengths
- 3D similarity map using UMAP and MiniLM embeddings enables intuitive browsing
- Semantic search goes beyond keyword matching
- Structured summaries (problem/approach/result/novelty) extract key insights
- Affiliation trend panels offer a high-level view of participating institutions
Considerations
- Covers only ICML 2026 papers (no other conferences or years)
- Relies on OpenReview data and may inherit its completeness or timeliness
- No mention of offline access or API for programmatic use
README quick start
ICML 2026 Paper Explorer
Explore 8126 ICML 2026 papers (6341 main + 1785 workshop) with:
- 🌌 3D similarity map (UMAP + MiniLM embeddings)
- 🔍 Semantic search
- 📊 Rich summaries (problem/approach/result/novelty)
- 🏛️ Affiliation trend panels
- 🔖 Bookmarks + CSV export
- 📅 Conference day filtering
Data extracted from OpenReview. PDFs link to openreview.net.
Description
Explore 8126 ICML 2026 papers with 3D similarity map, semantic search, and rich summaries
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