Towards Release Strategy Optimization for Apps in Google Play. - Sheng Shen 001; Xuan Lu; Ziniu Hu; Xuanzhe Liu (Internetware, 2017)

Towards Release Strategy Optimization for Apps in Google Play. - Sheng Shen 001; Xuan Lu; Ziniu Hu (CoRR, 2017)

Ermes: Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification. - Zhenpeng Chen; Sheng Shen 001; Ziniu Hu; Xuan Lu; Qiaozhu Mei; Xuanzhe Liu (CoRR, 2018)

An annotated dataset of literary entities. - David Bamman; Sejal Popat; Sheng Shen 001 (NAACL-HLT (1), 2019)

Pragmatically Informative Text Generation. - Sheng Shen 001; Daniel Fried; Jacob Andreas; Dan Klein 001 (NAACL-HLT (1), 2019)

Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification. - Zhenpeng Chen; Sheng Shen 001; Ziniu Hu; Xuan Lu; Qiaozhu Mei; Xuanzhe Liu (WWW, 2019)

Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT. - Sheng Shen 001; Zhen Dong 003; Jiayu Ye; Linjian Ma; Zhewei Yao; Amir Gholami; Michael W. Mahoney; Kurt Keutzer (AAAI, 2020)

On the Generation of Medical Question-Answer Pairs. - Sheng Shen 001; Yaliang Li; Nan Du 001; Xian Wu 001; Yusheng Xie; Shen Ge; Tao Yang 012; Kai Wang; Xingzheng Liang; Wei Fan 001 (AAAI, 2020)

MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding. - Qinxin Wang; Hao Tan 002; Sheng Shen 001; Michael W. Mahoney; Zhewei Yao (EMNLP (1), 2020)

Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers. - Zhuohan Li 001; Eric Wallace; Sheng Shen 001; Kevin Lin; Kurt Keutzer; Dan Klein 001; Joey Gonzalez (ICML, 2020)

PowerNorm: Rethinking Batch Normalization in Transformers. - Sheng Shen 001; Zhewei Yao; Amir Gholami; Michael W. Mahoney; Kurt Keutzer (ICML, 2020)

Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification (Extended Abstract). - Zhenpeng Chen; Sheng Shen 001; Ziniu Hu; Xuan Lu; Qiaozhu Mei; Xuanzhe Liu (IJCAI, 2020)

Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers. - Zhuohan Li 001; Eric Wallace; Sheng Shen 001; Kevin Lin; Kurt Keutzer; Dan Klein 001; Joseph E. Gonzalez (CoRR, 2020)

Rethinking Batch Normalization in Transformers. - Sheng Shen 001; Zhewei Yao; Amir Gholami; Michael W. Mahoney; Kurt Keutzer (CoRR, 2020)

ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning. - Zhewei Yao; Amir Gholami; Sheng Shen 001; Kurt Keutzer; Michael W. Mahoney (CoRR, 2020)

Reservoir Transformer. - Sheng Shen 001; Alexei Baevski; Ari S. Morcos; Kurt Keutzer; Michael Auli; Douwe Kiela (CoRR, 2020)

ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning. - Zhewei Yao; Amir Gholami; Sheng Shen 001; Mustafa Mustafa; Kurt Keutzer; Michael W. Mahoney (AAAI, 2021)

Reservoir Transformers. - Sheng Shen 001; Alexei Baevski; Ari S. Morcos; Kurt Keutzer; Michael Auli; Douwe Kiela (ACL/IJCNLP (1), 2021)

What's Hidden in a One-layer Randomly Weighted Transformer? - Sheng Shen 001; Zhewei Yao; Douwe Kiela; Kurt Keutzer; Michael W. Mahoney (EMNLP (1), 2021)

Discovering Non-monotonic Autoregressive Orderings with Variational Inference. - Xuanlin Li; Brandon Trabucco; Dong Huk Park; Michael Luo; Sheng Shen 001; Trevor Darrell; Yang Gao 029 (ICLR, 2021)

Noisy Self-Knowledge Distillation for Text Summarization. - Yang Liu 124; Sheng Shen 001; Mirella Lapata (NAACL-HLT, 2021)

MLPruning: A Multilevel Structured Pruning Framework for Transformer-based Models. - Zhewei Yao; Linjian Ma; Sheng Shen 001; Kurt Keutzer; Michael W. Mahoney (CoRR, 2021)

Learned Token Pruning for Transformers. - Sehoon Kim 001; Sheng Shen 001; David Thorsley; Amir Gholami; Joseph Hassoun; Kurt Keutzer (CoRR, 2021)

Multitask Prompted Training Enables Zero-Shot Task Generalization. - Victor Sanh; Albert Webson; Colin Raffel; Stephen H. Bach; Lintang Sutawika; Zaid Alyafeai; Antoine Chaffin; Arnaud Stiegler; Teven Le Scao; Arun Raja; Manan Dey; M. Saiful Bari; et al. (CoRR, 2021)

What Language Model to Train if You Have One Million GPU Hours? - Teven Le Scao; Thomas Wang; Daniel Hesslow; Lucile Saulnier; Stas Bekman; M. Saiful Bari; Stella Biderman; Hady Elsahar; Niklas Muennighoff; Jason Phang; Ofir Press; Colin Raffel; et al. (EMNLP (Findings), 2022)

Multitask Prompted Training Enables Zero-Shot Task Generalization. - Victor Sanh; Albert Webson; Colin Raffel; Stephen H. Bach; Lintang Sutawika; Zaid Alyafeai; Antoine Chaffin; Arnaud Stiegler; Arun Raja; Manan Dey; M Saiful Bari; Canwen Xu; et al. (ICLR, 2022)

How Much Can CLIP Benefit Vision-and-Language Tasks? - Sheng Shen 001; Liunian Harold Li; Hao Tan 002; Mohit Bansal; Anna Rohrbach; Kai-Wei Chang; Zhewei Yao; Kurt Keutzer (ICLR, 2022)

Staged Training for Transformer Language Models. - Sheng Shen 001; Pete Walsh; Kurt Keutzer; Jesse Dodge; Matthew E. Peters; Iz Beltagy (ICML, 2022)

Learned Token Pruning for Transformers. - Sehoon Kim 001; Sheng Shen 001; David Thorsley; Amir Gholami; Woosuk Kwon; Joseph Hassoun; Kurt Keutzer (KDD, 2022)

Exposing the Limits of Video-Text Models through Contrast Sets. - Jae Sung Park; Sheng Shen 001; Ali Farhadi; Trevor Darrell; Yejin Choi 001; Anna Rohrbach (NAACL-HLT, 2022)

K-LITE: Learning Transferable Visual Models with External Knowledge. - Sheng Shen 001; Chunyuan Li; Xiaowei Hu 006; Yujia Xie; Jianwei Yang; Pengchuan Zhang; Zhe Gan; Lijuan Wang; Lu Yuan; Ce Liu 001; Kurt Keutzer; Trevor Darrell; et al. (NeurIPS, 2022)

Crosslingual Generalization through Multitask Finetuning. - Niklas Muennighoff; Thomas Wang; Lintang Sutawika; Adam Roberts; Stella Biderman; Teven Le Scao; M. Saiful Bari; Sheng Shen 001; Zheng Xin Yong; Hailey Schoelkopf; Xiangru Tang; Dragomir Radev; et al. (ACL (1), 2023)

Scaling Vision-Language Models with Sparse Mixture of Experts. - Sheng Shen 001; Zhewei Yao; Chunyuan Li; Trevor Darrell; Kurt Keutzer; Yuxiong He (EMNLP (Findings), 2023)

Poisoning Language Models During Instruction Tuning. - Alexander Wan; Eric Wallace; Sheng Shen 001; Dan Klein 001 (ICML, 2023)

Large Language Models are Visual Reasoning Coordinators. - Liangyu Chen 005; Bo Li 080; Sheng Shen 001; Jingkang Yang; Chunyuan Li; Kurt Keutzer; Trevor Darrell; Ziwei Liu 002 (NeurIPS, 2023)

Flan-MoE: Scaling Instruction-Finetuned Language Models with Sparse Mixture of Experts. - Sheng Shen 001; Le Hou; Yanqi Zhou; Nan Du 002; Shayne Longpre; Jason Wei; Hyung Won Chung; Barret Zoph; William Fedus; Xinyun Chen; Tu Vu; Yuexin Wu; et al. (CoRR, 2023)

HallE-Switch: Rethinking and Controlling Object Existence Hallucinations in Large Vision Language Models for Detailed Caption. - Bohan Zhai; Shijia Yang; Xiangchen Zhao; Chenfeng Xu; Sheng Shen 001; Dongdi Zhao; Kurt Keutzer; Manling Li; Tan Yan; Xiangjun Fan (CoRR, 2023)

From Text to Tactic: Evaluating LLMs Playing the Game of Avalon. - Jonathan Light; Min Cai; Sheng Shen 001; Ziniu Hu (CoRR, 2023)

LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement. - Nicholas Lee; Thanakul Wattanawong; Sehoon Kim 001; Karttikeya Mangalam; Sheng Shen 001; Gopala Anumanchipalli; Michael W. Mahoney; Kurt Keutzer; Amir Gholami (ACL (Findings), 2024)

Aligning Large Multimodal Models with Factually Augmented RLHF. - Zhiqing Sun; Sheng Shen 001; Shengcao Cao; Haotian Liu; Chunyuan Li; Yikang Shen; Chuang Gan 001; Liangyan Gui; Yu-Xiong Wang; Yiming Yang; Kurt Keutzer; Trevor Darrell (ACL (Findings), 2024)

AgentBench: Evaluating LLMs as Agents. - Xiao Liu 036; Hao Yu; Hanchen Zhang; Yifan Xu; Xuanyu Lei; Hanyu Lai; Yu Gu 016; Hangliang Ding; Kaiwen Men; Kejuan Yang; Shudan Zhang; Xiang Deng 001; et al. (ICLR, 2024)

Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for Large Language Models. - Sheng Shen 001; Le Hou; Yanqi Zhou; Nan Du 002; Shayne Longpre; Jason Wei; Hyung Won Chung; Barret Zoph; William Fedus; Xinyun Chen; Tu Vu; Yuexin Wu; et al. (ICLR, 2024)

SqueezeLLM: Dense-and-Sparse Quantization. - Sehoon Kim 001; Coleman Hooper; Amir Gholami; Zhen Dong 003; Xiuyu Li; Sheng Shen 001; Michael W. Mahoney; Kurt Keutzer (ICML, 2024)

MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens. - Anas Awadalla; Le Xue; Oscar Lo; Manli Shu; Hannah Lee; Etash Kumar Guha; Sheng Shen 001; Mohamed Awadalla; Silvio Savarese; Caiming Xiong; Ran Xu 001; Yejin Choi 001; et al. (NeurIPS, 2024)

Enhancing Large Vision Language Models with Self-Training on Image Comprehension. - Yihe Deng; Pan Lu; Fan Yin; Ziniu Hu; Sheng Shen 001; Quanquan Gu; James Y. Zou; Kai-Wei Chang; Wei Wang 010 (NeurIPS, 2024)

Multitask Vision-Language Prompt Tuning. - Sheng Shen 001; Shijia Yang; Tianjun Zhang; Bohan Zhai; Joseph E. Gonzalez; Kurt Keutzer; Trevor Darrell (WACV, 2024)

RAFT: Adapting Language Model to Domain Specific RAG. - Tianjun Zhang; Shishir G. Patil; Naman Jain; Sheng Shen 001; Matei Zaharia; Ion Stoica; Joseph E. Gonzalez (CoRR, 2024)

Enhancing Large Vision Language Models with Self-Training on Image Comprehension. - Yihe Deng; Pan Lu; Fan Yin; Ziniu Hu; Sheng Shen 001; James Zou 001; Kai-Wei Chang; Wei Wang 010 (CoRR, 2024)

MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens. - Anas Awadalla; Le Xue; Oscar Lo; Manli Shu; Hannah Lee; Etash Kumar Guha; Matt Jordan; Sheng Shen 001; Mohamed Awadalla; Silvio Savarese; Caiming Xiong; Ran Xu 001; et al. (CoRR, 2024)

BLIP3-KALE: Knowledge Augmented Large-Scale Dense Captions. - Anas Awadalla; Le Xue; Manli Shu; An Yan; Jun Wang 090; Senthil Purushwalkam; Sheng Shen 001; Hannah Lee; Oscar Lo; Jae Sung Park; Etash Kumar Guha; Silvio Savarese; et al. (CoRR, 2024)

Efficient and Scalable Large Multimodal Models - Sheng Shen 001 (2024)