Summary of Study Of the Proper Nnue Dataset, by Daniel Tan et al.
Study of the Proper NNUE Dataset
by Daniel Tan, Neftali Watkinson Medina
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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 neural network architecture called NNUE has greatly impacted chess engine development by enabling top engines to maintain competitive performance. The creation of high-quality datasets is crucial for training NNUE models, particularly in complex domains like chess where tactical and strategic evaluations are vital. Despite its importance, the methods for constructing effective datasets remain poorly understood and under-documented. This paper proposes an algorithm for generating and filtering datasets composed of “quiet” positions that are stable and free from tactical volatility. The approach provides a clear methodology for dataset creation, which can be replicated and generalized across various evaluation functions. Testing demonstrates significant improvements in engine performance, confirming the effectiveness of our method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NNUE has changed how chess engines work, making top engines better. To make NNUE work well, you need good data. But creating that data is tricky, especially when it comes to complex things like chess. This paper helps by showing a new way to make and filter datasets with “quiet” positions that are stable and not full of tricks. It shows how to do this in a way that can be used for different evaluation methods. The results show that using this method makes the engines better, which is important. |
Keywords
» Artificial intelligence » Neural network
