Exponential machines

A. Novikov, M. Trofimov, I. Oseledets

    Research output: Contribution to journalArticlepeer-review

    6 Citations (Scopus)


    Modeling interactions between features improves the performance of machine learning solutions in many domains (e.g. recommender systems or sentiment analysis). In this paper, we introduce Exponential machines (ExM), a predictor that models all interactions of every order. The key idea is to represent an exponentially large tensor of parameters in a factorized format called tensor train (TT). The tensor train format regularizes the model and lets you control the number of underlying parameters. To train the model, we develop a stochastic Riemannian optimization procedure, which allows us to fit tensors with ¼ 2 56 entries. We show that the model achieves state-of-the-art performance on synthetic data with high-order interactions and that it works on par with high-order factorization machines on a recommender system dataset MovieLens 100 K.

    Original languageEnglish
    Pages (from-to)789-797
    Number of pages9
    JournalBulletin of the Polish Academy of Sciences: Technical Sciences
    Publication statusPublished - 2018


    • Factorization machines
    • Riemannian optimization
    • Tensor decomposition
    • Tensor train


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