YerevaNN /jɛɾɛvɑnˈɛn/ is a non-profit computer science and mathematics research lab based in Yerevan, Armenia.
Research
- Machine learning algorithms
- [2023] Scaling laws for mixed-modal language models.
[preprint]
(with Meta AI)
- [2022] Matching map recovery with an unknown number of outliers.
Accepted at AISTATS'23
[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)
- [2021] 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
- 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.
The handwritten digits in the background are generated by deep convolutional generative adversarial networks [paper] [code]