Summary of Training Artificial Neural Networks by Coordinate Search Algorithm, By Ehsan Rokhsatyazdi et al.
Training Artificial Neural Networks by Coordinate Search Algorithm
by Ehsan Rokhsatyazdi, Shahryar Rahnamayan, Sevil Zanjani Miyandoab, Azam Asilian Bidgoli, H.R. Tizhoosh
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 abstract presents a novel approach to training artificial neural networks (ANNs) using a gradient-free optimization algorithm, specifically an instance of General Pattern Search methods called Coordinate Search (CS). The authors highlight the limitations of traditional gradient-based learning methods like Stochastic Gradient Descent (SGD), including requirements for differentiable activation functions and inability to optimize multiple non-differentiable loss functions simultaneously. To address these concerns, the proposed CS algorithm is tailored to multi-objective/multi-loss problems and can be used with non-differentiable activation functions. The authors demonstrate the effectiveness of their method in finding reasonable solutions with fewer function calls, outperforming gradient-based approaches in some cases, particularly when working with insufficient labeled training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to train artificial neural networks (ANNs) is being developed. Right now, we rely on algorithms like Stochastic Gradient Descent (SGD) or Adam to do this job. But these methods have some limitations. They need special “activation functions” that can be easily changed and they’re not good at solving multiple problems at the same time. The new method uses a different approach called Coordinate Search (CS). It’s like a map that helps find the best solution without needing all those things. The results show that this new way can sometimes work better than the old ways, especially when we don’t have enough information to train the network. |
Keywords
* Artificial intelligence * Optimization * Stochastic gradient descent
