Summary of On the Role Of Initialization on the Implicit Bias in Deep Linear Networks, by Oria Gruber et al.
On the Role of Initialization on the Implicit Bias in Deep Linear Networks
by Oria Gruber, Haim Avron
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
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 In this research paper, the authors aim to shed light on the theoretical efficacy of Deep Learning (DL) models, specifically exploring the implicit bias that allows them to generalize effectively despite being designed for perfect data fitting. By investigating various sources of implicit bias, including weight initialization, the study examines how deep networks solve underdetermined linear systems and its impact on optimization and generalization. The findings provide insights into DL’s performance characteristics, contributing to a deeper understanding of its success. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep Learning models are super smart because they can learn from data and make predictions, even when it’s not perfect. But how do they do this? Scientists are trying to figure out why Deep Learning works so well, despite being designed to fit the data exactly. One thing that might help is the way we start off the learning process, by initializing the weights of our neural networks. This study looks at how different starting points can affect how well our models perform and generalize. By studying this, we can get a better understanding of why Deep Learning works so well. |
Keywords
* Artificial intelligence * Deep learning * Generalization * Optimization
