Jan 17, 2013 · We propose a new class of models which aim to provide improved performance while retaining many of the benefits of the existing class of embedding models.

However, despite being scalable to large datasets they do not take full advantage of the extra data due to their linear nature, and we believe they typically ...

Our approach works by reweighting each component of the embedding of features and labels with a potentially nonlinear affinity function. We describe several ...

We propose a new class of models which aim to provide improved performance while retaining many of the bene- fits of the existing class of embedding models. Our ...

Affinity Weighted Embedding. Jason Weston. Ron Weiss · Hector Yee. International Conference on Learning Representations (2013). Download Google Scholar.

Jan 17, 2013 · Our new approach works by iteratively learning a linear embedding model where the next iteration's features and labels are reweighted as a ...

This work proposes a new class of models which aim to provide improved performance while retaining many of the benefits of the existing class of embedding ...

Mar 18, 2013 · We propose a new class of models which aim to provide improved performance while retaining many of the benefits of the existing class of embedding models.

Affinity Weighted Embedding ... Supervised (linear) embedding models like Wsabie and PSI have proven successful at ranking, recommendation and annotation tasks.

Jun 21, 2014 · Our approach works by reweighting each component of the embedding of features and labels with a potentially nonlinear affinity function. We ...