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technologyreview.com·3h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
SVRG and Beyond via Posterior Correction
TL;DR
Stochastic Variance Reduced Gradient (SVRG) methods have not been fundamentally linked to Bayesian techniques, creating a gap in understanding. This work establishes a connection between SVRG and a Bayesian method called 'posterior correction', showing that SVRG can be viewed as a specific instance of this approach.
✦ Why It Matters
Engineers can leverage Bayesian insights to improve the efficiency and robustness of SVRG-based training methods.
Key Takeaways
- Explore the connection between SVRG and Bayesian methods.
- Implement the new Newton-like SVRG variant for better optimization.
- Utilize the Adam-like extension for large-scale training tasks.
- Consider using flexible exponential-family posteriors in your models.
How It Works
The paper connects SVRG to Bayesian posterior correction by demonstrating that SVRG can be derived from isotropic-Gaussian posteriors. This insight allows for the development of new SVRG variants that utilize more complex exponential-family posteriors, leading to improved optimization techniques.