Unlike traditional HiRAG systems (like RAPTOR) that rely on slow and expensive LLM summarization, Hierarchical Vector Quantization RAG uses a Vector-Centric Representative Selection strategy combined ...

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 ...

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 ...

Abstract: For uniform scalar quantization, the error distribution is approximately a uniform distribution over an interval (which is also a 1-dimensional ball ...

Vector quantisation and its associated learning algorithms form an essential framework within modern machine learning, providing interpretable and computationally efficient methods for data ...

Abstract: This letter presents a finite-rate deep-learning (DL)-based channel state information (CSI) feedback method for massive multiple-input multiple-output (MIMO) systems. The presented method ...

This paper discusses three basic blocks for the inference of convolutional neural networks (CNNs). Pyramid Vector Quantization [1] (PVQ) is discussed as an effective quantizer for CNNs weights ...

Autoregressive LLMs are complex neural networks that generate coherent and contextually relevant text through sequential prediction. These LLms excel at handling large datasets and are very strong at ...