Summary of Additive Regularization Schedule For Neural Architecture Search, by Mark Potanin and Kirill Vayser and Vadim Strijov
Additive regularization schedule for neural architecture search
by Mark Potanin, Kirill Vayser, Vadim Strijov
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 A novel approach to optimizing neural network structures is presented in this paper, which addresses the critical impact of neural architecture on forecasting accuracy and stability. By proposing a loss function with additive regularizer elements, each representing a criterion for optimization, the authors demonstrate how to construct an optimal neural network structure that balances quality criteria. A schedule-driven regularization procedure iteratively adjusts the set of regularizers to optimize various parts of the structure, leading to efficient and accurate networks with low complexity. Experimental results show the proposed method outperforms non-regularized models on a range of datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding the best way to build artificial neural networks that can make good predictions. Right now, building these networks can be tricky because it’s hard to know what structure they should have. The authors propose a new approach to solve this problem by using something called a “loss function” with different parts that help guide the network’s development. They show how this method can create efficient and accurate networks that are also simple. |
Keywords
» Artificial intelligence » Loss function » Neural network » Optimization » Regularization
