Summary of Convergence Of Gradient Descent For Recurrent Neural Networks: a Nonasymptotic Analysis, by Semih Cayci et al.
Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis
by Semih Cayci, Atilla Eryilmaz
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 analyzes recurrent neural networks with diagonal hidden-to-hidden weight matrices, trained with gradient descent in a supervised learning setting. It proves that gradient descent can achieve optimality without massive overparameterization. The analysis provides improved bounds on network size and identifies the impact of long-term dependencies on convergence and network width. Notably, it shows that an initialized recurrent neural network can achieve optimality with a logarithmic scaling of network size with sample size. This contrasts with prior works requiring high-order polynomial dependency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies special kinds of neural networks called recurrent neural networks (RNNs) that are trained using a type of optimization algorithm called gradient descent. It shows that RNNs can be very good at learning patterns in data without needing to have too many “neurons” or connections. This is important because it means that we might not need as much computational power to use these networks for things like language processing and time series forecasting. |
Keywords
* Artificial intelligence * Gradient descent * Neural network * Optimization * Supervised * Time series
