t-SNE
t-SNE on Beyond Market Intelligence: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on t-sne in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around t-sne, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
![GPT-2 Small’s embedding geometry around “Trump”: discretized vs. continuous nearest neighbours [P]](https://preview.redd.it/tlvz4c3i32eh1.png?width=640&crop=smart&auto=webp&s=aad6aeec9197e26debda00093dd47611e70c5a08)
GPT-2 Small’s embedding geometry around “Trump”: discretized vs. continuous nearest neighbours [P]
This visualization offers a compelling look into GPT-2 Small’s foundational understanding of language. Examining the token "Trump" within its static embedding table reveals a fascinating distinction: nearest neighbors shift dramatically depending on whether the embedding space is treated as continuous or discretized. The continuous representation yields a surprisingly specific group – family, staff, rivals, and former presidents like Obama and Eisenhower – while discretization produces broader political terms.
Interactive map of GPT-2's token embedding space - tap any token and explore [P]
Explore the intricate landscape of GPT-2's token embeddings with this interactive map, a compelling visualization of 32,070 alphabetic tokens from GPT-2-small. Accessible on mobile, the tool allows users to tap any token and discover its nearest connections, effectively "walking the graph" through real nearest-kin relationships identified via a minimum spanning tree. This innovative display, submitted by /u/Limp-Contest-7309, offers a unique perspective on language model structure—a deeper dive into GPT-2's vocabulary is available in our related article, "GPT-2 Small’s embedding geometry around “Trump.”