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Articles · Rebeauty Atlas

HUM AI SCI

AB Abhishekh

Non-Negative Matrix Factorization: A Complete Derivation from First Principles

Every formula proved. No gaps. No “it can be shown that…”

Apr 14, 2026 · 11 min read · 3 claps #nmf #low-rank-approximation #mathematics

Non-Negative Matrix Factorization: A Complete Derivation from First Principles

HUM AI

FA Farshad Noravesh

Drawbacks of Low Rank Methods for Attention

Low-rank approximation methods are widely used to reduce the computational complexity and memory requirements of large-scale problems…

May 28, 2025 · 3 min read · 1 claps #low-rank-approximation #attention #transformers

Drawbacks of Low Rank Methods for Attention

HUM MDA AI SCI TCH

AR Arik Poznanski

Low-Rank Factorization in PyTorch: Compressing Neural Networks with Linear Algebra

Originally published at https://arikpoz.github.io.

May 02, 2025 · 10 min read · 0 claps #deep-learning #low-rank-approximation #machine-learning

Low-Rank Factorization in PyTorch: Compressing Neural Networks with Linear Algebra

VE VectorWorks Academy

Ace AI Interview Series 25 — Achieving Sub-Quadratic Attention

For the complete list of AI interview prep topics, check out the Table of Contents for this comprehensive AI interview prep series …

Feb 28, 2025 · 3 min read · 0 claps paywalled #sparse-attention #low-rank-approximation #memory-efficient

Ace AI Interview Series 25 — Achieving Sub-Quadratic Attention

SCI DSN

HA Harriet

Visualizing SVD (SVD-Vis): A Deep Dive into Low-Rank Matrix Approximation

Singular Value Decomposition (SVD) is one of the most powerful tools in linear algebra, with applications ranging from dimensionality…

Feb 17, 2025 · 2 min read · 51 claps #svd #low-rank-approximation

Visualizing SVD (SVD-Vis): A Deep Dive into Low-Rank Matrix Approximation

VE VectorWorks Academy

Ace AI Interview Series 7— Understanding the Bottleneck of Multi-Head Attention and How to Mitigate…

For the complete list of AI interview prep topics, check out the Table of Contents for this comprehensive AI interview prep series …

Nov 27, 2024 · 3 min read · 5 claps paywalled #multi-head-attention #sparse-attention #low-rank-approximation

HUM MDA AI

AN Anish Hilary

CNN size reduction : Approximating Color Channels and Clustering higher layers.

A technique to compress CNN model using SVD

May 30, 2024 · 8 min read · 1 claps #cnn-compression #svd #filter-clustering

CNN size reduction : Approximating Color Channels and Clustering higher layers.

SCI DSN

HI Hiroshi Wayama

[Linear Algebra] Singular Value Decomposition and Low Rank Approximation

Singular value decomposition and low-rank approximation

May 22, 2024 · 6 min read · 13 claps #python #recommender-systems #linear-algebra

MDA AI SCI DSN

AN Anish Hilary

Low rank Approximation for 4D kernels in Convolutional Neural Networks through SVD

In this blog post, we’ll explore a research paper that demonstrates the use of Singular Value Decomposition (SVD) to decompose 4D kernels…

May 14, 2024 · 6 min read · 5 claps #low-rank-approximation #svd #cnn-decomposition

Low rank Approximation for 4D kernels in Convolutional Neural Networks through SVD

HUM AI MDA TCH

SA Sanjay Basu, PhD · my_aiml

On ‘Compression Algorithms for Large Language Models’ — shrinking the model size and reducing the…

Last week, I came across a paper published by esteemed researchers at Seoul National University, South Korea. The paper is a comprehensive…

Feb 04, 2024 · 6 min read · 42 claps paywalled #large-language-models #compression-techniques #model-quantization