Summary of Caadam: Improving Adam Optimizer Using Connection Aware Methods, by Remi Genet and Hugo Inzirillo
CaAdam: Improving Adam optimizer using connection aware methods
by Remi Genet, Hugo Inzirillo
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The proposed CaAdam algorithm enhances the traditional Adam optimizer by considering the architectural specifics of neural networks. By introducing connection-aware optimization through carefully designed proxies of structural information, CaAdam dynamically adjusts learning rates based on layer depth, connection counts, and gradient distributions. This approach enables more granular optimization while working within the constraints of current deep learning frameworks. Experimental results on standard datasets such as CIFAR-10 and Fashion MNIST show that CaAdam achieves faster convergence and higher accuracy compared to the standard Adam optimizer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make neural networks learn better is introduced, called CaAdam. It’s like a superpower for the usual Adam algorithm. Instead of just looking at the numbers and formulas, CaAdam also looks at the structure of the network, like how many connections there are between layers. This helps it find the best way to change the weights in the network to make it work better. Tests on common datasets show that CaAdam is faster and more accurate than the usual Adam algorithm. |
Keywords
» Artificial intelligence » Deep learning » Optimization
