<ul><li>Embeddings are a basic feature extraction step in many machine learning models, especially in natural language processing.</li><li>A probability model is used to study the learning capability of embeddings, where the correlation of random variables is related to the similarity of the embeddings.</li><li>The low-rank approximate message passing (AMP) method can be used to learn the embeddings.</li><li>The theoretical findings are validated through simulations on synthetic and real text data.</li></ul>

Estimation of embedding vectors in high dimensions

Discover more