Hellinger Distance google
In probability and statistics, the Hellinger distance (also called Bhattacharyya distance as this was originally introduced by Anil Kumar Bhattacharya) is used to quantify the similarity between two probability distributions. It is a type of f-divergence. The Hellinger distance is defined in terms of the Hellinger integral, which was introduced by Ernst Hellinger in 1909.[1][2] …

Semantic Entity Retrieval Toolkit (SERT) google
Unsupervised learning of low-dimensional, semantic representations of words and entities has recently gained attention. In this paper we describe the Semantic Entity Retrieval Toolkit (SERT) that provides implementations of our previously published entity representation models. The toolkit provides a unified interface to different representation learning algorithms, fine-grained parsing configuration and can be used transparently with GPUs. In addition, users can easily modify existing models or implement their own models in the framework. After model training, SERT can be used to rank entities according to a textual query and extract the learned entity/word representation for use in downstream algorithms, such as clustering or recommendation. …

Field-aware Factorization Machines (FFM) google
Field-aware factorization machines (FFM) have been used to win two click-through rate prediction competitions hosted by Criteo and Avazu. In these slides we introduce the formulation of FFM together with well known linear model, degree-2 polynomial model, and factorization machines. …

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