| Libri-Light: A Benchmark for ASR with Limited or No Supervision J Kahn, M Rivière, W Zheng, E Kharitonov, Q Xu, PE Mazaré, J Karadayi, ... ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 729 | 2020 |
| End-to-End ASR: from Supervised to Semi-Supervised Learning with Modern Architectures G Synnaeve, Q Xu, J Kahn, E Grave, T Likhomanenko, V Pratap, A Sriram, ... arXiv preprint arXiv:1911.08460, 2019 | 282 | 2019 |
| Self-Training for End-to-End Speech Recognition J Kahn, A Lee, A Hannun arXiv preprint arXiv:1909.09116, 2019 | 268 | 2019 |
| Robust wav2vec 2.0: Analyzing domain shift in self-supervised pre-training WN Hsu, A Sriram, A Baevski, T Likhomanenko, Q Xu, V Pratap, J Kahn, ... arXiv preprint arXiv:2104.01027, 2021 | 260 | 2021 |
| Wav2Letter++: A Fast Open-source Speech Recognition System V Pratap, A Hannun, Q Xu, J Cai, J Kahn, G Synnaeve, V Liptchinsky, ... ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 244 | 2019 |
| Iterative pseudo-labeling for speech recognition Q Xu, T Likhomanenko, J Kahn, A Hannun, G Synnaeve, R Collobert arXiv preprint arXiv:2005.09267, 2020 | 154 | 2020 |
| Chameleon: Mixed-modal early-fusion foundation models C Team arXiv preprint arXiv:2405.09818, 2024 | 146 | 2024 |
| Ra-dit: Retrieval-augmented dual instruction tuning XV Lin, X Chen, M Chen, W Shi, M Lomeli, R James, P Rodriguez, J Kahn, ... arXiv preprint arXiv:2310.01352, 2023 | 109 | 2023 |
| Rethinking evaluation in asr: Are our models robust enough? T Likhomanenko, Q Xu, V Pratap, P Tomasello, J Kahn, G Avidov, ... arXiv preprint arXiv:2010.11745, 2020 | 106 | 2020 |
| Transfusion: Predict the next token and diffuse images with one multi-modal model C Zhou, L Yu, A Babu, K Tirumala, M Yasunaga, L Shamis, J Kahn, X Ma, ... arXiv preprint arXiv:2408.11039, 2024 | 85 | 2024 |
| slimipl: Language-model-free iterative pseudo-labeling T Likhomanenko, Q Xu, J Kahn, G Synnaeve, R Collobert arXiv preprint arXiv:2010.11524, 2020 | 67 | 2020 |
| Scaling Up Online Speech Recognition Using ConvNets V Pratap, Q Xu, J Kahn, G Avidov, T Likhomanenko, A Hannun, ... | 49 | 2020 |
| Differentiable weighted finite-state transducers A Hannun, V Pratap, J Kahn, WN Hsu arXiv preprint arXiv:2010.01003, 2020 | 35 | 2020 |
| Flashlight: Enabling innovation in tools for machine learning JD Kahn, V Pratap, T Likhomanenko, Q Xu, A Hannun, J Cai, P Tomasello, ... International Conference on Machine Learning, 10557-10574, 2022 | 26 | 2022 |
| TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch J Hwang, M Hira, C Chen, X Zhang, Z Ni, G Sun, P Ma, R Huang, V Pratap, ... 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 1-9, 2023 | 16 | 2023 |
| Reasoning over public and private data in retrieval-based systems S Arora, P Lewis, A Fan, J Kahn, C Ré Transactions of the Association for Computational Linguistics 11, 902-921, 2023 | 15 | 2023 |
| OLLA: Decreasing the Memory Usage of Neural Networks by Optimizing the Lifetime and Location of Arrays. B Steiner, M Elhoushi, J Kahn, J Hegarty CoRR, 2022 | 14* | 2022 |
| Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM S Sukhbaatar, O Golovneva, V Sharma, H Xu, XV Lin, B Rozière, J Kahn, ... arXiv preprint arXiv:2403.07816, 2024 | 8 | 2024 |
| The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment J Fernandez, J Kahn, C Na, Y Bisk, E Strubell arXiv preprint arXiv:2302.06117, 2023 | 3 | 2023 |
| Characterizing and efficiently accelerating multimodal generation model inference Y Lee, A Sun, B Hosmer, B Acun, C Balioglu, C Wang, CD Hernandez, ... arXiv preprint arXiv:2410.00215, 2024 | 2 | 2024 |
Qiantong XuGoogle DeepmindVerified email at google.com
