Summary of New Logarithmic Step Size For Stochastic Gradient Descent, by M. Soheil Shamaee et al.
New logarithmic step size for stochastic gradient descent
by M. Soheil Shamaee, S. Fathi Hafshejani, Z. Saeidian
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper proposes a novel warm restart technique using a logarithmic step size for stochastic gradient descent (SGD), which achieves an O(1/√T) convergence rate on smooth and non-convex functions. The authors demonstrate the efficiency of this approach on the FashionMinst, CIFAR10, and CIFAR100 datasets and show that it improves test accuracy by 0.9% for the CIFAR100 dataset when using a convolutional neural network (CNN) model compared to nine other existing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn better from mistakes in machine learning. It introduces a new way to make sure our models don’t get stuck and can improve over time. The authors test this new approach on different types of images and show that it does better than many other methods. This is important because it means we might be able to build even better AI systems that can recognize objects or do other tasks more accurately. |
Keywords
» Artificial intelligence » Cnn » Machine learning » Neural network » Stochastic gradient descent
