1 citations · 3 across the 7 of their papers we have counts for
7 papers
Exact Permutation Recovery Under Unknown Scalar Affine Transformation
Tigran Galstyan, Avetik Karagulyan, Arshak Minasyan
We study the problem of matching two sets of noisy feature vectors when underlying true features are related by an unknown scalar affine transformation. Our method comprises two pr…
Do Satellite Tasks Need Special Pretraining?
Ani Vanyan, Alvard Barseghyan, Hakob Tamazyan +4
Foundation models have advanced machine learning across various modalities, including images. Recently multiple teams trained foundation models specialized for remote sensing appli…
Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution
Elen Vardanyan, Sona Hunanyan, Tigran Galstyan +2
This paper explores the problem of generative modeling, aiming to simulate diverse examples from an unknown distribution based on observed examples. While recent studies have focus…
Matching Map Recovery with an Unknown Number of Outliers
Arshak Minasyan, Tigran Galstyan, Sona Hunanyan +1
We consider the problem of finding the matching map between two sets of -dimensional noisy feature-vectors. The distinctive feature of our setting is that we do not assume that…
Failure Modes of Domain Generalization Algorithms
Tigran Galstyan, Hrayr Harutyunyan, Hrant Khachatrian +2
Domain generalization algorithms use training data from multiple domains to learn models that generalize well to unseen domains. While recently proposed benchmarks demonstrate that…
Optimal detection of the feature matching map in presence of noise and outliers
Tigran Galstyan, Arshak Minasyan, Arnak Dalalyan
We consider the problem of finding the matching map between two sets of dimensional vectors from noisy observations, where the second set contains outliers. The matching map is…
Robust Classification under Class-Dependent Domain Shift
Tigran Galstyan, Hrant Khachatrian, Greg Ver Steeg +1
Investigation of machine learning algorithms robust to changes between the training and test distributions is an active area of research. In this paper we explore a special type of…