🏢 Department of Computer Science, City University of Hong Kong
Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning
·1305 words·7 mins·
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Natural Language Processing
Large Language Models
🏢 Department of Computer Science, City University of Hong Kong
Transformers excel at in-context learning (ICL), solving new tasks with just prompts. This paper provides a mathematical explanation, showing how transformers use multi-concept word semantics to achie…
Flatten Anything: Unsupervised Neural Surface Parameterization
·2390 words·12 mins·
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Computer Vision
3D Vision
🏢 Department of Computer Science, City University of Hong Kong
Flatten Anything Model (FAM) revolutionizes neural surface parameterization with unsupervised learning, handling complex topologies and unstructured data fully automatically.