Shang-Wen Daniel Li
Shang-Wen Daniel Li
FAIR - Research manager
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Cited by
Cited by
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
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
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
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
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
Supporting Clustering with Contrastive Learning
D Zhang, F Nan, X Wei, S Li, H Zhu, K McKeown, R Nallapati, A Arnold, ...
NAACL 2021, 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
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
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
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
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), 2023
Introducing meta llama 3: The most capable openly available llm to date
AI Meta
Meta AI, 2024
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
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
Pairwise supervised contrastive learning of sentence representations
D Zhang, SW Li, W Xiao, H Zhu, R Nallapati, AO Arnold, B Xiang
EMNLP 2021, 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
Leveraging video interaction data and content analysis to improve video learning
J Kim, SW Li, C Cai, K Gajos, R Miller
CHI 2014 Workshop on Learning Innovation at Scale, 2014
Speechprompt: An exploration of prompt tuning on generative spoken language model for speech processing tasks
KW Chang, WC Tseng, SW Li, H Lee
arXiv preprint arXiv:2203.16773, 2022
Meta learning for natural language processing: A survey
H Lee, SW Li, NT Vu
arXiv preprint arXiv:2205.01500, 2022
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, 2024
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