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mit.edu
Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples | MIT Visualization Group
Embeddings mapping high-dimensional discrete input to lower-dimensional continuous vector spaces have been widely adopted in machine learning applications as a way to capture domain semantics. Interviewing 13 embedding users across disciplines, we find comparing embeddings is a key task for deployment or downstream analysis but unfolds in a ...
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New short course! Embedding Models: From Architecture to Implementation. Learn the details of the architecture and capabilities of embedding models, and build and train a simple dual encoder model. Built in collaboration with Vectara, and taught by Ofer Mendelevitch, Vectara’s Head of Developer Relations. In detail: 📚 Learn about word embedding, sentence embedding, and cross-encoder models, and how to use them in RAG 🤖 Understand how transformer models like BERT are trained and used in semanti
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