YerevaNN

research lab

YerevaNN /jɛɾɛvɑnˈɛn/ is a non-profit computer science and mathematics research lab based in Yerevan, Armenia.

Research

  • Machine learning algorithms
    • [2022] Matching map recovery with an unknown number of outliers. [preprint] (with ENSAE-CREST)
    • [2022] GradSkip: an extension of a local gradient method for distributed optimization that supports variable number of local gradient steps in each communication round. [preprint] (with KAUST)
    • [2022] Characterization of the failure modes of domain generalization algorithms. Published in CVPR'22 (with USC ISI).
    • [2021] WARP: a parameter-efficient method for transfer learning in NLP. Published in ACL'21 (with USC ISI)
    • [2021] Theoretical analysis of the detection of the feature matching map in presence of outliers. Published in Electronic Journal of Statistics (with ENSAE-CREST)
    • [2021] A survey of deep neural networks for semi-supervised image classification. Published in JUCS.
    • [2020] Robust classification under class-dependent domain shift [preprint] (with USC ISI). Presented at ICML 2020 UDL Workshop
    • [2020] A novel robust estimator of the mean of a multivariate Gaussian distribution. Published in Annals of Statistics (with ENSAE-CREST)
    • [2019] T-Corex: a novel method for temporal covariance estimation using information theoretic apparatus [preprint] [code] (with USC ISI)
  • Machine learning for biomedical data
  • Development of Armenian treebanks
    • Eastern Armenian treebank as part of the Universal Dependencies project [data (52K tokens)] [website] [code]
    • An end-to-end syntax parser for Armenian [code] [demo]
    • Collaboration with Marat Yavrumyan and Anna Danielyan from Yerevan State University. Partially funded by ANSEF and ISTC.
  • Student projects
    • [2019] Morpheme-aware word vectors [paper] [code]
    • [2018] A joint POS tagger and lemmatizer [paper] [code]
    • [2018] Reproducing DIIN network for NLI [report] [code]
    • [2017] Reproducing R-NET network for QA [blogpost] [code]
Visit our blog and GitHub for more.

Team

Hrant Khachatrian / Github / Google Scholar
Karen Hambardzumyan / Github / Google Scholar
Tigran Galstyan / Github / Google Scholar
Ani Vanyan / Github
Hovhannes Tamoyan / Github
Khazhak Galstyan / Github
Ani Tevosyan / Github
Knarik Mheryan / Github
Gayane Chilingaryan / Github
Arto Maranjyan / Google Scholar
Rafayel Darbinyan / Github
Davit Papikyan / Github
Alumni: Hrayr Harutyunyan, Gor Arakelyan, Martin Mirakyan, Ashot Matevosyan, Arshak Minasyan Please fill in this form if you are interested in joining YerevaNN.

The handwritten digits in the background are generated by deep convolutional generative adversarial networks [paper] [code]