Summary of Effect Of Random Learning Rate: Theoretical Analysis Of Sgd Dynamics in Non-convex Optimization Via Stationary Distribution, by Naoki Yoshida et al.
Effect of Random Learning Rate: Theoretical Analysis of SGD Dynamics in Non-Convex Optimization via Stationary Distribution
by Naoki Yoshida, Shogo Nakakita, Masaaki Imaizumi
First submitted to arxiv on: 23 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 study proposes a novel variant of stochastic gradient descent (SGD), called Poisson SGD, which employs a random learning rate. The authors demonstrate that under weak assumptions on the loss function, the distribution of updated parameters by Poisson SGD converges to a stationary distribution. Moreover, they show that Poisson SGD can find global minima in non-convex optimization problems and evaluate its generalization error. To achieve this, the researchers approximate the distribution by Poisson SGD with that of the bouncy particle sampler (BPS) and derive its stationary distribution using piece-wise deterministic Markov process (PDMP). This work contributes to the convergence properties of stochastic optimization algorithms, particularly in deep learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores a new way to improve a popular machine learning algorithm called stochastic gradient descent. The team proposes a new version of this algorithm that uses random learning rates and shows that it can find the best solution in complex problems. They also test how well this new algorithm performs compared to other methods. This study helps us understand how these algorithms work and can be used in various applications, such as image recognition or natural language processing. |
Keywords
» Artificial intelligence » Deep learning » Generalization » Loss function » Machine learning » Natural language processing » Optimization » Stochastic gradient descent
