YerevaNN /jɛɾɛvɑnˈɛn/ is a non-profit machine learning research lab based in Yerevan, Armenia.
Read more
about our plans to establish an AI Institute.
Research
Machine learning for biomedical data
Machine learning under domain shift
- [2023] Identifying and disentangling spurious features in pretrained image representations.
[preprint]
(with Meta AI and USC ISI).
Accepted at ICML 2023 Workshop on Spurious Correlations, Invariance and Stability.
- [2021] Characterization of the failure modes of domain generalization algorithms.
Published in CVPR'22
(with USC ISI).
- [2020] Robust classification under class-dependent domain shift
[preprint]
(with USC ISI). Presented at ICML 2020
UDL Workshop
Machine learning algorithms
- [2023] Scaling laws for mixed-modal language models.
[preprint]
(with Meta AI)
- [2023] Wireless non-line-of-sight localization of a device using a single antenna in an urban environment
Accepted at IEEE Big Data Service 2023.
[preprint]
(with Yerevan State University and
CNR Institute of Informatics and Telematics)
- [2022] Matching map recovery with an unknown number of outliers.
Published in 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] 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] 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)
Development of Armenian treebanks
The handwritten digits in the background are generated by deep convolutional generative adversarial networks [paper] [code]