Categories: FAANG

On Information Geometry and Iterative Optimization in Model Compression: Operator Factorization

The ever-increasing parameter counts of deep learning models necessitate effective compression techniques for deployment on resource-constrained devices. This paper explores the application of information geometry, the study of density-induced metrics on parameter spaces, to analyze existing methods within the space of model compression, primarily focusing on operator factorization. Adopting this perspective highlights the core challenge: defining an optimal low-compute submanifold (or subset) and projecting onto it. We argue that many successful model compression approaches can be understood…

The “Super Weight:” How Even a Single Parameter can Determine a Large Language Model’s Behavior

A recent paper from Apple researchers, "The Super Weight in Large Language Models," reveals that an extremely small subset of parameters in LLMs (in some cases, a single parameter) can exert a disproportionate influence on an LLM’s overall functionality (see Figure 1). This work highlights the critical role of these…

August 22, 2025

In "FAANG"

On a Neural Implementation of Brenier’s Polar Factorization

In 1991, Brenier proved a theorem that generalizes the polar decomposition for square matrices -- factored as PSD ×times× unitary -- to any vector field F:Rd→RdF:mathbb{R}^drightarrow mathbb{R}^dF:Rd→Rd. The theorem, known as the polar factorization theorem, states that any field FFF can be recovered as the composition of the gradient of…

July 17, 2024

In "FAANG"

Rethinking Non-Negative Matrix Factorization with Implicit Neural Representations

This paper was accepted at the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) 2025 Non-negative Matrix Factorization (NMF) is a powerful technique for analyzing regularly-sampled data, i.e., data that can be stored in a matrix. For audio, this has led to numerous applications using time-frequency…

August 19, 2025

In "FAANG"

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