Summary of Beyond Uniform Scaling: Exploring Depth Heterogeneity in Neural Architectures, by Akash Guna R.t et al.
Beyond Uniform Scaling: Exploring Depth Heterogeneity in Neural Architectures
by Akash Guna R.T, Arnav Chavan, Deepak Gupta
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 AI research paper introduces an automated scaling approach for neural networks, specifically focusing on vision transformers. The conventional method of scaling involves designing a base network and growing different dimensions by predefined factors. However, this new method leverages second-order loss landscape information to scale the network flexibly, even with skip connections. The training-aware approach jointly scales and trains the transformer without requiring additional iterations. The paper hypothesizes that not all neurons need uniform depth complexity, embracing instead depth heterogeneity. Evaluations on DeiT-S with ImageNet100 show a 2.5% accuracy gain and 10% parameter efficiency improvement over conventional scaling. Scaled networks demonstrate superior performance when training small-scale datasets from scratch. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This AI research paper makes neural networks more efficient! It’s like building a Lego tower – you can make it taller or wider, but this new approach lets you change the shape of each block to get better results. The idea is that not all parts of the network need to be the same size or complexity. This helps when training smaller datasets from scratch, and it even works with special connections between layers. The researchers tested their method on a big dataset called ImageNet100 and found it was 2.5% more accurate and used 10% fewer calculations than before. |
Keywords
* Artificial intelligence * Transformer
