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Summary of Low Rank Multi-dictionary Selection at Scale, by Boya Ma et al.


Low Rank Multi-Dictionary Selection at Scale

by Boya Ma, Maxwell McNeil, Abram Magner, Petko Bogdanov

First submitted to arxiv on: 11 Jun 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: None

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The sparse dictionary coding framework has been used for various types of signals, including images, time series, graph signals, and recently 2-way spatio-temporal data. It’s a powerful tool that can represent complex signals using a linear combination of predefined atoms. However, large dictionaries can be challenging to scale, especially when working with multiple dictionaries. This paper tackles the problem of scaling multi-dictionary coding for big datasets and dictionaries.
Low GrooveSquid.com (original content) Low Difficulty Summary
In this paper, researchers use sparse dictionary coding to represent complex signals like 2D spatio-temporal data. They’ve been able to improve models using large dictionaries, but it’s hard to make them work with lots of data. The goal is to find a way to make these models work well even when dealing with big datasets.

Keywords

» Artificial intelligence  » Time series