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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| 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
