| Videoclip: Contrastive pre-training for zero-shot video-text understanding H Xu, G Ghosh, PY Huang, D Okhonko, A Aghajanyan, F Metze, ... arXiv preprint arXiv:2109.14084, 2021 | 253 | 2021 |
| Muppet: Massive multi-task representations with pre-finetuning A Aghajanyan, A Gupta, A Shrivastava, X Chen, L Zettlemoyer, S Gupta arXiv preprint arXiv:2101.11038, 2021 | 201 | 2021 |
| Incoder: A generative model for code infilling and synthesis D Fried, A Aghajanyan, J Lin, S Wang, E Wallace, F Shi, R Zhong, W Yih, ... arXiv preprint arXiv:2204.05999, 2022 | 191 | 2022 |
| Better fine-tuning by reducing representational collapse A Aghajanyan, A Shrivastava, A Gupta, N Goyal, L Zettlemoyer, S Gupta arXiv preprint arXiv:2008.03156, 2020 | 166 | 2020 |
| Intrinsic dimensionality explains the effectiveness of language model fine-tuning A Aghajanyan, L Zettlemoyer, S Gupta arXiv preprint arXiv:2012.13255, 2020 | 154 | 2020 |
| Pre-training via paraphrasing M Lewis, M Ghazvininejad, G Ghosh, A Aghajanyan, S Wang, ... Advances in Neural Information Processing Systems 33, 18470-18481, 2020 | 122 | 2020 |
| Cm3: A causal masked multimodal model of the internet A Aghajanyan, B Huang, C Ross, V Karpukhin, H Xu, N Goyal, D Okhonko, ... arXiv preprint arXiv:2201.07520, 2022 | 68 | 2022 |
| Memorization without overfitting: Analyzing the training dynamics of large language models K Tirumala, A Markosyan, L Zettlemoyer, A Aghajanyan Advances in Neural Information Processing Systems 35, 38274-38290, 2022 | 56 | 2022 |
| Conversational semantic parsing A Aghajanyan, J Maillard, A Shrivastava, K Diedrick, M Haeger, H Li, ... arXiv preprint arXiv:2009.13655, 2020 | 48 | 2020 |
| HTLM: Hyper-text pre-training and prompting of language models A Armen, O Dmytro, L Mike, J Mandar, H Xu, G Gargi International Conference on Learning Representations, 2022 | 42* | 2022 |
| Improving passage retrieval with zero-shot question generation DS Sachan, M Lewis, M Joshi, A Aghajanyan, W Yih, J Pineau, ... arXiv preprint arXiv:2204.07496, 2022 | 29 | 2022 |
| On-device convolutional neural network models for assistant systems A Aly, A Babu, A Aghajanyan US Patent 11,314,941, 2022 | 23 | 2022 |
| Semantic representations using structural ontology for assistant systems A Aghajanyan, S Gupta, B Moran, TF Levin, CANSH Nakatsu, D Difranco, ... US Patent 11,651,449, 2023 | 22 | 2023 |
| Softtarget regularization: An effective technique to reduce over-fitting in neural networks A Aghajanyan 2017 3rd IEEE International Conference on Cybernetics (CYBCONF), 1-5, 2017 | 20 | 2017 |
| Scaling laws for generative mixed-modal language models A Aghajanyan, L Yu, A Conneau, WN Hsu, K Hambardzumyan, S Zhang, ... arXiv preprint arXiv:2301.03728, 2023 | 18 | 2023 |
| Non-autoregressive semantic parsing for compositional task-oriented dialog A Babu, A Shrivastava, A Aghajanyan, A Aly, A Fan, M Ghazvininejad arXiv preprint arXiv:2104.04923, 2021 | 17 | 2021 |
| Retronlu: Retrieval augmented task-oriented semantic parsing V Gupta, A Shrivastava, A Sagar, A Aghajanyan, D Savenkov arXiv preprint arXiv:2109.10410, 2021 | 16 | 2021 |
| Retrieval-augmented multimodal language modeling M Yasunaga, A Aghajanyan, W Shi, R James, J Leskovec, P Liang, ... | 10 | 2023 |
| Megabyte: Predicting million-byte sequences with multiscale transformers L Yu, D Simig, C Flaherty, A Aghajanyan, L Zettlemoyer, M Lewis arXiv preprint arXiv:2305.07185, 2023 | 10 | 2023 |
| Convolution aware initialization A Aghajanyan arXiv preprint arXiv:1702.06295, 2017 | 9 | 2017 |
Luke ZettlemoyerUniversity of Washington; Meta AIVerified email at cs.washington.edu
