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

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 ...

Abstract: Normalizing flows, a category of probabilistic models famed for their capabilities in modeling complex data distributions, have exhibited remarkable efficacy in unsupervised anomaly ...

Abstract: In Genome-Wide Association Studies (GWAS) huge amounts of genetic information are analyzed in order to discover how the observed variations, more specifically, the Single Nucleotide ...

Time series forecasting faces significant challenges due to highly heterogeneous distributions across domains and limited data coverage of real-world scenarios. UniVQ addresses these challenges ...