| The llama 3 herd of models A Grattafiori, A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, ... arXiv preprint arXiv:2407.21783, 2024 | 3667 | 2024 |
| Multitask prompted training enables zero-shot task generalization V Sanh, A Webson, C Raffel, SH Bach, L Sutawika, Z Alyafeai, A Chaffin, ... ICLR 2022, 2021 | 1846 | 2021 |
| Bloom: A 176b-parameter open-access multilingual language model T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... | 1814 | 2023 |
| Crosslingual generalization through multitask finetuning N Muennighoff, T Wang, L Sutawika, A Roberts, S Biderman, TL Scao, ... ACL 2023, 2022 | 732 | 2022 |
| Q-bert: Hessian based ultra low precision quantization of bert S Shen, Z Dong, J Ye, L Ma, Z Yao, A Gholami, MW Mahoney, K Keutzer AAAI 2020, 2019 | 627 | 2019 |
| How Much Can CLIP Benefit Vision-and-Language Tasks? S Shen*, LH Li*, H Tan, M Bansal, A Rohrbach, KW Chang, Z Yao, ... ICLR 2022, 2021 | 466 | 2021 |
| Agentbench: Evaluating llms as agents X Liu, H Yu, H Zhang, Y Xu, X Lei, H Lai, Y Gu, H Ding, K Men, K Yang, ... arXiv preprint arXiv:2308.03688, 2023 | 447* | 2023 |
| Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers Z Li*, E Wallace*, S Shen*, K Lin*, K Keutzer, D Klein, JE Gonzalez ICML 2020, 2020 | 345 | 2020 |
| Llavanext: Improved reasoning, ocr, and world knowledge H Liu, C Li, Y Li, B Li, Y Zhang, S Shen, YJ Lee | 331 | 2024 |
| ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning Z Yao, A Gholami, S Shen, K Keutzer, MW Mahoney AAAI 2021, 2020 | 309 | 2020 |
| Aligning large multimodal models with factually augmented rlhf Z Sun*, S Shen*, S Cao*, H Liu, C Li, Y Shen, C Gan, LY Gui, YX Wang, ... arXiv preprint arXiv:2309.14525, 2023 | 279 | 2023 |
| SqueezeLLM: Dense-and-Sparse Quantization S Kim*, C Hooper*, A Gholami*, Z Dong, X Li, S Shen, MW Mahoney, ... arXiv preprint arXiv:2306.07629, 2023 | 201 | 2023 |
| Poisoning Language Models During Instruction Tuning A Wan*, E Wallace*, S Shen, D Klein ICML 2023, 2023 | 186 | 2023 |
| Learned token pruning for transformers S Kim*, S Shen*, D Thorsley, A Gholami, W Kwon, J Hassoun, K Keutzer KDD 2022, 2021 | 173 | 2021 |
| Raft: Adapting language model to domain specific rag T Zhang, SG Patil, N Jain, S Shen, M Zaharia, I Stoica, JE Gonzalez First Conference on Language Modeling, 2024 | 160 | 2024 |
| What Language Model to Train if You Have One Million GPU Hours? T Le Scao, T Wang, D Hesslow, L Saulnier, S Bekman, MS Bari, ... EMNLP 2022, 2022 | 122 | 2022 |
| An annotated dataset of literary entities D Bamman, S Popat, S Shen NAACL 2019, 2019 | 120 | 2019 |
| K-lite: Learning transferable visual models with external knowledge S Shen, C Li, X Hu, Y Xie, J Yang, P Zhang, A Rohrbach, Z Gan, L Wang, ... NeurIPS 2022, 2022 | 103 | 2022 |
| Powernorm: Rethinking batch normalization in transformers S Shen, Z Yao, A Gholami, M Mahoney, K Keutzer ICML 2020, 2020 | 100 | 2020 |
| Scaling Vision-Language Models with Sparse Mixture of Experts S Shen, Z Yao, C Li, T Darrell, K Keutzer, Y He EMNLP 2023 Findings, 2023 | 92 | 2023 |
Kurt KeutzerProfessor of the Graduate School, EECS, University of California, BerkeleyE-mailová adresa ověřena na: berkeley.edu
