Summary of Fixed Width Treelike Neural Networks Capacity Analysis — Generic Activations, by Mihailo Stojnic
Fixed width treelike neural networks capacity analysis – generic activations
by Mihailo Stojnic
First submitted to arxiv on: 8 Feb 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 This paper investigates the capacity of treelike committee machines (TCM) neural networks. Building on previous work using Random Duality Theory (RDT), the authors show that their frameworks can handle more general types of activations, including quadratic and rectified linear unit (ReLU). The paper provides upper bound characterizations for memory capacities in terms of hidden layer neurons (d) for each activation type. Notably, the results reveal a decrease in bounding capacity with increasing d, converging to a constant value, and the maximum capacity is achieved with two hidden layer neurons. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well neural networks called treelike committee machines can learn from data. It shows that these networks can work well even when they use different types of “hidden” math functions, like quadratic or ReLU. The researchers found out some surprising things about what happens when the network gets bigger – it actually gets worse at learning! But in a weird way, it’s better with just two hidden layers. This is important because it helps us understand how these networks work and how we can make them better. |
Keywords
* Artificial intelligence * Relu
