AI’s “backbone” increasingly means energy, infrastructure, and matrix math powering massive next-generation computing systems.
A from-scratch PyTorch implementation of TurboQuant (ICLR 2026), Google's two-stage vector quantization algorithm for compressing LLM key-value caches — enhanced with a comprehensive, research-grade ...
sciNMF is an R package designed for exploring the heterogeneity of cellular transcriptional states across individuals using single-cell RNA-seq data. This package is developed by HuangLab at the ...
Abstract: This paper introduces singular value decomposition (SVD), a major matrix decomposition technique. SVD serves as the underlining computational engine of many other techniques such as ...
Understanding Singular Value Decomposition If you have a matrix A with dim = n, it is possible to compute n eigenvalues (ordinary numbers like 1.234) and n associated eigenvectors, each with n values.
In the past few decades, multi-linear algebra also known as tensor algebra has been adapted and employed as a tool for various engineering applications. Recent developments in tensor algebra have ...
Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on an implementation of the technique that emphasizes simplicity and ease-of-modification over robustness and ...
Abstract: Substantial progress has been made recently on developing provably accurate and efficient algorithms for low-rank matrix factorization via nonconvex optimization. While conventional wisdom ...
The records of the interesting genes expressions have stimulated new development of analysis techniques and subsequently understanding the gene co-expressions. However, a simple method to extract the ...
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