5 papers
With Great Backbones Comes Great Adversarial Transferability
Erik Arakelyan, Karen Hambardzumyan, Davit Papikyan +4
Advances in self-supervised learning (SSL) for machine vision have improved representation robustness and model performance, giving rise to pre-trained backbones like \emph{ResNet}…
BARTSmiles: Generative Masked Language Models for Molecular Representations
Gayane Chilingaryan, Hovhannes Tamoyan, Ani Tevosyan +6
We discover a robust self-supervised strategy tailored towards molecular representations for generative masked language models through a series of tailored, in-depth ablations. Usi…
WARP: Word-level Adversarial ReProgramming
Karen Hambardzumyan, Hrant Khachatrian, Jonathan May
Transfer learning from pretrained language models recently became the dominant approach for solving many NLP tasks. A common approach to transfer learning for multiple tasks that m…
Towards JointUD: Part-of-speech Tagging and Lemmatization using Recurrent Neural Networks
Gor Arakelyan, Karen Hambardzumyan, Hrant Khachatrian
This paper describes our submission to CoNLL 2018 UD Shared Task. We have extended an LSTM-based neural network designed for sequence tagging to additionally generate character-lev…
Natural Language Inference over Interaction Space: ICLR 2018 Reproducibility Report
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 C…