# Chess2vec: Learning Vector Representations for Chess

arXiv: https://arxiv.org/abs/2011.01014 · HTML: https://mlsanity.com/paper/2011.01014/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [The Value of Chess Squares](https://mlsanity.com/paper/2307.05330/) (2023)
- [RePAIR: Predictive Self-Supervised Representation Learning in Chess](https://mlsanity.com/paper/2606.11860/) (2026)

## Abstract (arXiv)

We conduct the first study of its kind to generate and evaluate vector representations for chess pieces. In particular, we uncover the latent structure of chess pieces and moves, as well as predict chess moves from chess positions. We share preliminary results which anticipate our ongoing work on a neural network architecture that learns these embeddings directly from supervised feedback.