1 code implementation • 29 Apr 2017 • Sumit Sidana, Mikhail Trofimov, Oleg Horodnitskii, Charlotte Laclau, Yury Maximov, Massih-Reza Amini
The learning objective is based on three scenarios of ranking losses that control the ability of the model to maintain the ordering over the items induced from the users' preferences, as well as, the capacity of the dot-product defined in the learned embedded space to produce the ordering.
3 code implementations • 12 May 2016 • Alexander Novikov, Mikhail Trofimov, Ivan Oseledets
Modeling interactions between features improves the performance of machine learning solutions in many domains (e. g. recommender systems or sentiment analysis).
17 code implementations • 13 Nov 2015 • Mansour Ahmadi, Dmitry Ulyanov, Stanislav Semenov, Mikhail Trofimov, Giorgio Giacinto
This paradigm is presented and discussed in the present paper, where emphasis has been given to the phases related to the extraction, and selection of a set of novel features for the effective representation of malware samples.