Summary of Capacity Of the Hebbian-hopfield Network Associative Memory, by Mihailo Stojnic
Capacity of the Hebbian-Hopfield network associative memory
by Mihailo Stojnic
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Disordered Systems and Neural Networks (cond-mat.dis-nn); Information Theory (cs.IT); Machine Learning (cs.LG); Probability (math.PR)
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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 A neural network model with a Hebbian learning rule was introduced by Hopfield, allowing it to efficiently operate as an associative memory. The study found that if a small fraction of errors is tolerated in stored pattern retrieval, the network’s capacity scales linearly with each pattern’s size. Additionally, Hopfield predicted that the capacity ratio would approach 0.14 as the pattern size increases. This paper studies the same scenario using two famous pattern basins: AGS and NLT. By applying the fully lifted random duality theory (fl RDT), we obtain explicit capacity characterizations at the first level of lifting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers studied a type of neural network that can remember patterns very well. They looked at how this works when there are some mistakes allowed in remembering the patterns. The study found that the network’s ability to store memories grows as the size of each memory increases. A long time ago, someone predicted that the network’s capacity would be around 0.14 when the memories get very big. This new paper looks at the same situation but uses two different types of pattern basins: AGS and NLT. |
Keywords
* Artificial intelligence * Neural network
