156 citations · 156 across the 3 of their papers we have counts for
3 papers
cs.CL2023
MEMORY-VQ: Compression for Tractable Internet-Scale Memory
Yury Zemlyanskiy, Michiel de Jong, Luke Vilnis +4
Retrieval augmentation is a powerful but expensive method to make language models more knowledgeable about the world. Memory-based methods like LUMEN pre-compute token representati…
cs.CL2014★ 156 cited
Word Representations via Gaussian Embedding
Luke Vilnis, Andrew McCallum
Current work in lexical distributed representations maps each word to a point vector in low-dimensional space. Mapping instead to a density provides many interesting advantages, in…
cs.CL2014
Training for Fast Sequential Prediction Using Dynamic Feature Selection
Emma Strubell, Luke Vilnis, Andrew McCallum
We present paired learning and inference algorithms for significantly reducing computation and increasing speed of the vector dot products in the classifiers that are at the heart…