Summary of Growing Tiny Networks: Spotting Expressivity Bottlenecks and Fixing Them Optimally, by Manon Verbockhaven (tau et al.
Growing Tiny Networks: Spotting Expressivity Bottlenecks and Fixing Them Optimally
by Manon Verbockhaven, Sylvain Chevallier, Guillaume Charpiat, Théo Rudkiewicz
First submitted to arxiv on: 30 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 A novel approach in machine learning optimizes neural networks by adapting their architecture during training, rather than relying on costly hyperparameter searches. The traditional method of fixing a neural network’s architecture and optimizing its parameters can limit the expressivity of the function being learned. To overcome this, the proposed method detects and solves “expressivity bottlenecks” using backpropagation, allowing for the development of smaller networks that achieve similar results to larger ones. This technique is demonstrated on the CIFAR dataset, achieving competitive accuracy and training time while eliminating the need for hyperparameter search. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is a way for computers to learn from data without being explicitly programmed. Traditionally, people design complex neural networks and then adjust their settings to get the best results. However, this process can be slow and require a lot of trial-and-error. The new method in this paper allows the computer to change its own architecture during learning, making it more efficient and effective. By doing so, it can achieve similar results as larger networks but with less computation. |
Keywords
» Artificial intelligence » Backpropagation » Hyperparameter » Machine learning » Neural network
