no code implementations • 31 Jul 2023 • Elen Vardanyan, Arshak Minasyan, Sona Hunanyan, Tigran Galstyan, Arnak Dalalyan
Generative modeling is a widely-used machine learning method with various applications in scientific and industrial fields.
no code implementations • 24 Oct 2022 • Arshak Minasyan, Tigran Galstyan, Sona Hunanyan, Arnak Dalalyan
If $n$ and $m$ are the sizes of these two sets, we assume that the matching map that should be recovered is defined on a subset of unknown cardinality $k^*\le \min(n, m)$.
1 code implementation • CVPR 2022 • Tigran Galstyan, Hrayr Harutyunyan, Hrant Khachatrian, Greg Ver Steeg, Aram Galstyan
On Camelyon-17, domain-invariance degrades the quality of representations on unseen domains.
no code implementations • NeurIPS 2021 • Tigran Galstyan, Arshak Minasyan, Arnak Dalalyan
The matching map is then an injection, which can be consistently estimated only if the vectors of the second set are well separated.
no code implementations • 10 Jul 2020 • Tigran Galstyan, Hrant Khachatrian, Greg Ver Steeg, Aram Galstyan
Investigation of machine learning algorithms robust to changes between the training and test distributions is an active area of research.
1 code implementation • WS 2019 • Hrant Khachatrian, Lilit Nersisyan, Karen Hambardzumyan, Tigran Galstyan, Anna Hakobyan, Arsen Arakelyan, Andrey Rzhetsky, Aram Galstyan
Automatic extraction of relations and interactions between biological entities from scientific literature remains an extremely challenging problem in biomedical information extraction and natural language processing in general.