In an article recently posted to the Meta Research website, researchers focused on improving vector quantization for data compression and vector search. They introduced quantization with implicit ...

Imagine looking for similar things based on deeper insights instead of just keywords. That's what vector databases and similarity searches help with. Pangkalan data vektor enable vector similarity ...

Abstract: Remote estimation is vital in Internet of Things (IoT) networks. However, in multi-cell Fog Radio Access Networks (F-RAN), it faces significant challenges due to limited spectrum resources ...

Abstract: In neural audio coding, latent space quantization is often trained together with the rest of the model. In this work, we investigate the use of algebraic vector quantization (VQ) in a ...

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the probabilities of tokens occurring in a specific order is encoded. Billions of ...

TurboQuant vector quantization is Google Research’s latest bid to shrink the KV cache burden in LLM inference. Instead of focusing on model weights, the method targets runtime memory, with claims of ...

A vector quantization library originally transcribed from Deepmind's tensorflow implementation, made conveniently into a package. It uses exponential moving averages to update the dictionary. VQ has ...