| The llama 3 herd of models A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, A Letman, A Mathur, ... arXiv preprint arXiv:2407.21783, 2024 | 4122* | 2024 |
| Dinov2: Learning robust visual features without supervision M Oquab, T Darcet, T Moutakanni, H Vo, M Szafraniec, V Khalidov, ... arXiv preprint arXiv:2304.07193, 2023 | 3372* | 2023 |
| SUPERB: Speech processing Universal PERformance Benchmark S Yang, PH Chi, YS Chuang, CIJ Lai, K Lakhotia, YY Lin, AT Liu, J Shi, ... Interspeech 2021, 2021 | 1018 | 2021 |
| Introducing meta llama 3: The most capable openly available llm to date AI Meta Meta AI 2 (5), 6, 2024 | 484* | 2024 |
| Self-supervised speech representation learning: A review A Mohamed, H Lee, L Borgholt, JD Havtorn, J Edin, C Igel, K Kirchhoff, ... IEEE Journal of Selected Topics in Signal Processing 16 (6), 1179-1210, 2022 | 422 | 2022 |
| Tera: Self-supervised learning of transformer encoder representation for speech AT Liu, SW Li, H Lee IEEE/ACM Transactions on Audio, Speech, and Language Processing 29, 2351-2366, 2021 | 410 | 2021 |
| Supporting Clustering with Contrastive Learning D Zhang, F Nan, X Wei, S Li, H Zhu, K McKeown, R Nallapati, A Arnold, ... NAACL 2021, 2021 | 236 | 2021 |
| DiffCSE: Difference-based contrastive learning for sentence embeddings YS Chuang, R Dangovski, H Luo, Y Zhang, S Chang, M Soljačić, SW Li, ... arXiv preprint arXiv:2204.10298, 2022 | 229 | 2022 |
| Audio ALBERT: A Lite BERT for Self-supervised Learning of Audio Representation PH Chi, PH Chung, TH Wu, CC Hsieh, SW Li, H Lee SLT 2021, 2020 | 199 | 2020 |
| Chameleon: Mixed-modal early-fusion foundation models C Team arXiv preprint arXiv:2405.09818, 2024 | 196 | 2024 |
| Data-driven interaction techniques for improving navigation of educational videos J Kim, PJ Guo, CJ Cai, SW Li, KZ Gajos, RC Miller Proceedings of the 27th annual ACM symposium on User interface software and …, 2014 | 174 | 2014 |
| Demystifying clip data H Xu, S Xie, XE Tan, PY Huang, R Howes, V Sharma, SW Li, G Ghosh, ... arXiv preprint arXiv:2309.16671, 2023 | 155 | 2023 |
| Scaling autoregressive multi-modal models: Pretraining and instruction tuning L Yu, B Shi, R Pasunuru, B Muller, O Golovneva, T Wang, A Babu, B Tang, ... arXiv preprint arXiv:2309.02591 2 (3), 3, 2023 | 146 | 2023 |
| Lifelong pretraining: Continually adapting language models to emerging corpora X Jin, D Zhang, H Zhu, W Xiao, SW Li, X Wei, A Arnold, X Ren arXiv preprint arXiv:2110.08534, 2021 | 129 | 2021 |
| SeamlessM4T: Massively Multilingual & Multimodal Machine Translation L Barrault, YA Chung, MC Meglioli, D Dale, N Dong, PA Duquenne, ... arXiv preprint arXiv:2308.11596, 2023 | 126 | 2023 |
| SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities HS Tsai, HJ Chang, WC Huang, Z Huang, K Lakhotia, S Yang, S Dong, ... ACL 2022, 2022 | 110 | 2022 |
| The llama 3 herd of models, 2024b Z Rosnbrick, Z Wen, Z Yang, Z Zhao URL https://arxiv. org/abs/2407.21783, 2024 | 68* | 2024 |
| Mavil: Masked audio-video learners PY Huang, V Sharma, H Xu, C Ryali, Y Li, SW Li, G Ghosh, J Malik, ... Advances in Neural Information Processing Systems 36, 20371-20393, 2023 | 68 | 2023 |
| Semi-Supervised Spoken Language Understanding via Self-Supervised Speech and Language Model Pretraining CI Lai, YS Chuang, HY Lee, SW Li, J Glass ICASSP 2021, 2020 | 66 | 2020 |
| Ml-superb: Multilingual speech universal performance benchmark J Shi, D Berrebbi, W Chen, HL Chung, EP Hu, WP Huang, X Chang, ... arXiv preprint arXiv:2305.10615, 2023 | 63 | 2023 |
Hung-yi LeeNational Taiwan UniversityVerified email at ntu.edu.tw
