Search Results for author: Hrant Khachatrian

Found 11 papers, 9 papers with code

WARP: Word-level Adversarial ReProgramming

1 code implementation ACL 2021 Karen Hambardzumyan, Hrant Khachatrian, Jonathan May

Transfer learning from pretrained language models recently became the dominant approach for solving many NLP tasks.

Language Modelling Pretrained Language Models +2

Robust Classification under Class-Dependent Domain Shift

no code implementations10 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.

Classification General Classification +1

BioRelEx 1.0: Biological Relation Extraction Benchmark

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.

Natural Language Processing Relation Extraction

Efficient Covariance Estimation from Temporal Data

2 code implementations30 May 2019 Hrayr Harutyunyan, Daniel Moyer, Hrant Khachatrian, Greg Ver Steeg, Aram Galstyan

Estimating the covariance structure of multivariate time series is a fundamental problem with a wide-range of real-world applications -- from financial modeling to fMRI analysis.

Time Series

Natural Language Inference over Interaction Space: ICLR 2018 Reproducibility Report

1 code implementation9 Feb 2018 Martin Mirakyan, Karen Hambardzumyan, Hrant Khachatrian

We have tried to reproduce the results of the paper "Natural Language Inference over Interaction Space" submitted to ICLR 2018 conference as part of the ICLR 2018 Reproducibility Challenge.

Model Selection Natural Language Inference

Cannot find the paper you are looking for? You can Submit a new open access paper.