Tag
embeddings
- Aug 21, 2026 Similarity Metrics and the Normalisation They Require Why cosine, inner product, and Euclidean distance rank differently, what L2 normalisation changes about a vector index, and which failures come from mixing conventions. inference systems
- Mar 24, 2026 Distance Concentration and Why Tree Indexes Collapse in High Dimensions Why bounding-box and ball-tree pruning stops helping as dimensionality grows, what the concentration effect does to nearest-neighbor contrast, and which index families survive it. information retrieval
- Jan 19, 2026 Embedding Layers as Learned Lookup Tables An embedding table is a matrix indexed by token id whose rows receive sparse gradients; its memory arithmetic, initialization, and weight tying decide what a model can learn from rare inputs. deep learning
- Jan 8, 2026 Cosine Similarity and the Geometry of Normalized Embedding Vectors What the cosine score actually measures, why length normalization turns it into a plain dot product, and when that normalization destroys information the model encoded. information retrieval
- Jul 3, 2025 Chunk Boundaries Decide What Can Be Retrieved How fixed-size, sentence, and structural chunking place boundaries, what overlap recovers and what it duplicates, and why the split point is an index-time commitment. inference systems