#embedding

4 articles

01 ·RAG & Retrieval·★ MEMBER·PAPER·7 min read Paper Explained: Ovis-Embedding — One Ruler for Text, Images, Video, and Audio A from-scratch walkthrough of Ovis-Embedding, a model family that puts text, images, video, and audio into one vector space: the four-stage training recipe, the trick that keeps accuracy when you shorten the vectors, and what the paper does not show. 02 ·Paper Deep-Dives·★ MEMBER·PAPER·11 min read Paper Walkthrough — RoboTok: Mining the Web for Demonstrations That Move Like Yours A ground-up walkthrough of RoboTok (arXiv:2609.03199), a data engine that pulls manipulation-relevant human demonstrations out of web video. Covers how DTW defines 'similar motion', how that ranking is distilled into an embedding for inner-product search, and what the results do and don't show. 03 ·RAG & Retrieval·FREE·PAPER·6 min read Embeddings from Scratch — from word2vec Intuition to Contextual Embeddings Why does 'king − man + woman ≈ queen' actually work? A from-zero tour of embeddings: the map metaphor, the distributional hypothesis, the word2vec math, numpy code, contextual embeddings after BERT, and the knobs you touch in real RAG systems. 04 ·Linear Algebra·FREE·9 min read Linear Algebra for AI — What Vectors and Matrices Are Actually Doing You will never compute a determinant to read an AI paper. What you do need is two ideas: a vector is a coordinate where meaning lives, and a matrix is a machine that carries coordinates somewhere else. From why a dot product means similarity to reading the attention equation symbol by symbol.