Summary of Does Negative Sampling Matter? a Review with Insights Into Its Theory and Applications, by Zhen Yang et al.
Does Negative Sampling Matter? A Review with Insights into its Theory and Applications
by Zhen Yang, Ming Ding, Tinglin Huang, Yukuo Cen, Junshuai Song, Bin Xu, Yuxiao Dong, Jie Tang
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 research paper proposes a general framework that leverages negative sampling, a widely applied technique across machine learning, computer vision, natural language processing, data mining, and recommender systems. The authors investigate the development of negative sampling through five evolutionary paths, categorizing strategies used to select negative sample candidates into global, local, mini-batch, hop, and memory-based approaches. They also review current negative sampling methods, including static, hard, GAN-based, Auxiliary-based, and In-batch methods, providing a clear structure for understanding the technique’s applications and benefits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a important idea in computer science called negative sampling. It’s used to help machines learn from data more efficiently. The researchers looked at how this idea has developed over time and grouped different ways of doing it into categories. They also showed where this technique is being used, like in image recognition or language processing. This can help us understand what makes it useful and how we might use it even better in the future. |
Keywords
* Artificial intelligence * Gan * Machine learning * Natural language processing
