Summary of Dynamical Stability and Chaos in Artificial Neural Network Trajectories Along Training, by Kaloyan Danovski et al.
Dynamical stability and chaos in artificial neural network trajectories along training
by Kaloyan Danovski, Miguel C. Soriano, Lucas Lacasa
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Disordered Systems and Neural Networks (cond-mat.dis-nn); Chaotic Dynamics (nlin.CD); Data Analysis, Statistics and Probability (physics.data-an)
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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 paper explores the training process of artificial neural networks as a dynamical system in graph space. By analyzing the network trajectories of a shallow neural network learning a simple classification task, researchers uncover hints of regular and chaotic behavior depending on the learning rate regime. The findings challenge common wisdom on convergence properties of neural networks and offer insights into the interplay between machine learning, network theory, and dynamical systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how training an artificial neural network is like a journey through “network space”. Researchers study this process by looking at how the network changes as it learns to do a simple task. They find that the way the network behaves depends on the rate at which it learns. This is interesting because it connects two fields, machine learning and dynamical systems. |
Keywords
» Artificial intelligence » Classification » Machine learning » Neural network
