Armen Aghajanyan

According to our database, Armen Aghajanyan authored at least 32 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

BartSmiles: Generative Masked Language Models for Molecular Representations.

Gayane Chilingaryan, Hovhannes Tamoyan, Ani Tevosyan, Nelly Babayan, Karen Hambardzumyan, Zaven Navoyan, Armen Aghajanyan, Hrant Khachatrian, Lusine Khondkaryan

J. Chem. Inf. Model., 2024

When Worse is Better: Navigating the compression-generation tradeoff in visual tokenization.

Vivek Ramanujan, Kushal Tirumala, Armen Aghajanyan, Luke Zettlemoyer, Ali Farhadi

CoRR, 2024

MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts.

Xi Victoria Lin, Akshat Shrivastava, Liang Luo, Srinivasan Iyer, Mike Lewis, Gargi Ghosh, Luke Zettlemoyer, Armen Aghajanyan

CoRR, 2024

Small Molecule Optimization with Large Language Models.

Philipp Guevorguian, Menua Bedrosian, Tigran Fahradyan, Gayane Chilingaryan, Hrant Khachatrian, Armen Aghajanyan

CoRR, 2024

Text Quality-Based Pruning for Efficient Training of Language Models.

Vasu Sharma, Karthik Padthe, Newsha Ardalani, Kushal Tirumala, Russell Howes, Hu Xu, Po-Yao Huang, Shang-Wen Li, Armen Aghajanyan, Gargi Ghosh, Luke Zettlemoyer

CoRR, 2024

Jointly Training Large Autoregressive Multimodal Models.

Emanuele Aiello, Lili Yu, Yixin Nie, Armen Aghajanyan, Barlas Oguz

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

DOMINO: A Dual-System for Multi-step Visual Language Reasoning.

Peifang Wang, Olga Golovneva, Armen Aghajanyan, Xiang Ren, Muhao Chen, Asli Celikyilmaz, Maryam Fazel-Zarandi

CoRR, 2023

Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning.

Lili Yu, Bowen Shi, Ramakanth Pasunuru, Benjamin Muller, Olga Golovneva, Tianlu Wang, Arun Babu, Binh Tang, Brian Karrer, Shelly Sheynin, Candace Ross, Adam Polyak, Russell Howes, Vasu Sharma, Puxin Xu, Hovhannes Tamoyan, Oron Ashual, Uriel Singer, Shang-Wen Li, Susan Zhang, Richard James, Gargi Ghosh, Yaniv Taigman, Maryam Fazel-Zarandi, Asli Celikyilmaz, Luke Zettlemoyer, Armen Aghajanyan

CoRR, 2023

MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers.

Lili Yu, Daniel Simig, Colin Flaherty, Armen Aghajanyan, Luke Zettlemoyer, Mike Lewis

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

D4: Improving LLM Pretraining via Document De-Duplication and Diversification.

Kushal Tirumala, Daniel Simig, Armen Aghajanyan, Ari Morcos

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Retrieval-Augmented Multimodal Language Modeling.

Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Richard James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, Wen-Tau Yih

Proceedings of the International Conference on Machine Learning, 2023

Scaling Laws for Generative Mixed-Modal Language Models.

Armen Aghajanyan, Lili Yu, Alexis Conneau, Wei-Ning Hsu, Karen Hambardzumyan, Susan Zhang, Stephen Roller, Naman Goyal, Omer Levy, Luke Zettlemoyer

Proceedings of the International Conference on Machine Learning, 2023

InCoder: A Generative Model for Code Infilling and Synthesis.

Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, Mike Lewis

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

CM3: A Causal Masked Multimodal Model of the Internet.

Armen Aghajanyan, Bernie Huang, Candace Ross, Vladimir Karpukhin, Hu Xu, Naman Goyal, Dmytro Okhonko, Mandar Joshi, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer

CoRR, 2022

Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models.

Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, Armen Aghajanyan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training.

Patrick Huber, Armen Aghajanyan, Barlas Oguz, Dmytro Okhonko, Scott Yih, Sonal Gupta, Xilun Chen

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2022, 2022

HTLM: Hyper-Text Pre-Training and Prompting of Language Models.

Armen Aghajanyan, Dmytro Okhonko, Mike Lewis, Mandar Joshi, Hu Xu, Gargi Ghosh, Luke Zettlemoyer

Proceedings of the Tenth International Conference on Learning Representations, 2022

Improving Passage Retrieval with Zero-Shot Question Generation.

Devendra Singh Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, Luke Zettlemoyer

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

RetroNLU: Retrieval Augmented Task-Oriented Semantic Parsing.

Vivek Gupta, Akshat Shrivastava, Adithya Sagar, Armen Aghajanyan, Denis Savenkov

Proceedings of the 4th Workshop on NLP for Conversational AI, 2022

2021

Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog.

Arun Babu, Akshat Shrivastava, Armen Aghajanyan, Ahmed Aly, Angela Fan, Marjan Ghazvininejad

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

Better Fine-Tuning by Reducing Representational Collapse.

Armen Aghajanyan, Akshat Shrivastava, Anchit Gupta, Naman Goyal, Luke Zettlemoyer, Sonal Gupta

Proceedings of the 9th International Conference on Learning Representations, 2021

VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding.

Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, Christoph Feichtenhofer

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

Muppet: Massive Multi-task Representations with Pre-Finetuning.

Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, Xilun Chen, Luke Zettlemoyer, Sonal Gupta

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning.

Armen Aghajanyan, Sonal Gupta, Luke Zettlemoyer

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

2020

Conversational Semantic Parsing.

Armen Aghajanyan, Jean Maillard, Akshat Shrivastava, Keith Diedrick, Mike Haeger, Haoran Li, Yashar Mehdad, Ves Stoyanov, Anuj Kumar, Mike Lewis, Sonal Gupta

CoRR, 2020

Pre-training via Paraphrasing.

Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan, Sida Wang, Luke Zettlemoyer

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Conversational Semantic Parsing.

Armen Aghajanyan, Jean Maillard, Akshat Shrivastava, Keith Diedrick, Michael Haeger, Haoran Li, Yashar Mehdad, Veselin Stoyanov, Anuj Kumar, Mike Lewis, Sonal Gupta

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

2019

Towards Language Agnostic Universal Representations.

Armen Aghajanyan, Xia Song, Saurabh Tiwary

Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

2017

Convolution Aware Initialization.

Armen Aghajanyan

CoRR, 2017

Charged Point Normalization: An Efficient Solution to the Saddle Point Problem.

Armen Aghajanyan

Proceedings of the 5th International Conference on Learning Representations, 2017

SoftTarget Regularization: An Effective Technique to Reduce Over-Fitting in Neural Networks.

Armen Aghajanyan

Proceedings of the 3rd IEEE International Conference on Cybernetics, 2017

2015

Gravitational Clustering.

Armen Aghajanyan

CoRR, 2015