Summary of Learning the Sherrington-kirkpatrick Model Even at Low Temperature, by Gautam Chandrasekaran and Adam Klivans
Learning the Sherrington-Kirkpatrick Model Even at Low Temperature
by Gautam Chandrasekaran, Adam Klivans
First submitted to arxiv on: 17 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Statistics Theory (math.ST); Machine Learning (stat.ML)
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 In this paper, researchers tackle the challenge of learning the parameters of undirected graphical models or Markov Random Fields (MRFs) when edge weights are randomly chosen. For Ising models, they propose a multiplicative-weight update algorithm due to Klivans and Meka that efficiently learns the parameters in polynomial time for any inverse temperature up to a certain logarithmic bound. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how to learn the rules of an undirected graphical model when some connections between things are chosen randomly. They focus on Ising models, which are special types of these models. The researchers develop a new way to update the model’s parameters that works quickly and accurately for certain temperature levels. |
Keywords
* Artificial intelligence * Temperature
