Tianyi Zhang

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Kilian WeinbergerCornell UniversityEmail yang diverifikasi di cornell.edu
Tatsunori HashimotoAssistant Professor, StanfordEmail yang diverifikasi di stanford.edu
Percy LiangAssociate Professor of Computer Science, Stanford UniversityEmail yang diverifikasi di cs.stanford.edu
Felix WuGoogle DeepMindEmail yang diverifikasi di google.com
Ishaan GulrajaniOpenAIEmail yang diverifikasi di openai.com
Yann DuboisOpenAIEmail yang diverifikasi di stanford.edu
Carlos GuestrinProfessor, Stanford UniversityEmail yang diverifikasi di stanford.edu
Rohan TaoriAnthropicEmail yang diverifikasi di anthropic.com
Xuechen LiStanford UniversityEmail yang diverifikasi di cs.stanford.edu
Yoav ArtziCornell University; ASAPPEmail yang diverifikasi di cs.cornell.edu
Amauri H. SouzaFederal Institute of Ceará (IFCE)Email yang diverifikasi di ifce.edu.br
Christopher FiftyStanford UniversityEmail yang diverifikasi di cornell.edu
Tao YuPhD student, Cornell UniversityEmail yang diverifikasi di cornell.edu
Varsha KishoreCornell UniversityEmail yang diverifikasi di cornell.edu
Kathleen McKeownProfessor of Computer Science and Director, Data Science Institute, Columbia UniversityEmail yang diverifikasi di cs.columbia.edu
Faisal LadhakNvidiaEmail yang diverifikasi di nvidia.com
Esin DurmusAnthropicEmail yang diverifikasi di stanford.edu
Daniel FriedAssistant professor, Carnegie Mellon UniversityEmail yang diverifikasi di cs.cmu.edu
Tao YuAssistant Professor, Computer Science, University of Hong KongEmail yang diverifikasi di cs.hku.hk
Scott Wen-tau YihMeta FAIREmail yang diverifikasi di meta.com

Ikuti

Tianyi Zhang

Stanford University

Email yang diverifikasi di cornell.edu - Beranda

Machine Learning Natural Language Processing


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Bertscore: Evaluating text generation with bert T Zhang, V Kishore, F Wu, KQ Weinberger, Y Artzi arXiv preprint arXiv:1904.09675, 2019	5874	2019
On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021	4768	2021
Simplifying Graph Convolutional Networks F Wu, T Zhang, AH Souza Jr, C Fifty, T Yu, KQ Weinberger Proceedings of the 36th International Conference on Machine Learning, 2019	3921	2019
Stanford alpaca: An instruction-following llama model R Taori, I Gulrajani, T Zhang, Y Dubois, X Li, C Guestrin, P Liang, ...	2842*	2023
Holistic evaluation of language models P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ... arXiv preprint arXiv:2211.09110, 2022	1203	2022
Alpacaeval: An automatic evaluator of instruction-following models X Li, T Zhang, Y Dubois, R Taori, I Gulrajani, C Guestrin, P Liang, ...	507	2023
Benchmarking large language models for news summarization T Zhang, F Ladhak, E Durmus, P Liang, K McKeown, TB Hashimoto Transactions of the Association for Computational Linguistics 12, 39-57, 2024	476	2024
Revisiting few-sample BERT fine-tuning T Zhang, F Wu, A Katiyar, KQ Weinberger, Y Artzi arXiv preprint arXiv:2006.05987, 2020	471	2020
Alpacafarm: A simulation framework for methods that learn from human feedback Y Dubois, CX Li, R Taori, T Zhang, I Gulrajani, J Ba, C Guestrin, PS Liang, ... Advances in Neural Information Processing Systems 36, 2024	438	2024
Identifying mislabeled data using the area under the margin ranking G Pleiss, T Zhang, E Elenberg, KQ Weinberger Advances in Neural Information Processing Systems 33, 17044-17056, 2020	297	2020
DS-1000: A natural and reliable benchmark for data science code generation Y Lai, C Li, Y Wang, T Zhang, R Zhong, L Zettlemoyer, W Yih, D Fried, ... International Conference on Machine Learning, 18319-18345, 2023	225	2023
Evaluating verifiability in generative search engines NF Liu, T Zhang, P Liang arXiv preprint arXiv:2304.09848, 2023	192	2023
Stanford alpaca: an instruction-following llama model (2023) R Taori, I Gulrajani, T Zhang, Y Dubois, X Li, C Guestrin, P Liang, ... URL https://github. com/tatsu-lab/stanford_alpaca 1 (9), 2023	136	2023
SWALP: Stochastic Weight Averaging in Low-Precision Training G Yang, T Zhang, P Kirichenko, J Bai, AG Wilson, C De Sa Proceedings of the 36th International Conference on Machine Learning, 2019	114	2019
Decentralized training of foundation models in heterogeneous environments B Yuan, Y He, J Davis, T Zhang, T Dao, B Chen, PS Liang, C Re, C Zhang Advances in Neural Information Processing Systems 35, 25464-25477, 2022	89	2022
Coder reviewer reranking for code generation T Zhang, T Yu, T Hashimoto, M Lewis, W Yih, D Fried, S Wang International Conference on Machine Learning, 41832-41846, 2023	81	2023
Qpytorch: A low-precision arithmetic simulation framework T Zhang, Z Lin, G Yang, C De Sa 2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive …, 2019	75	2019
When do pre-training biases propagate to downstream tasks? a case study in text summarization F Ladhak, E Durmus, M Suzgun, T Zhang, D Jurafsky, K McKeown, ... Proceedings of the 17th Conference of the European Chapter of the …, 2023	50	2023
On the inductive bias of masked language modeling: From statistical to syntactic dependencies T Zhang, T Hashimoto arXiv preprint arXiv:2104.05694, 2021	34	2021
LADIS: Language disentanglement for 3D shape editing I Huang, P Achlioptas, T Zhang, S Tulyakov, M Sung, L Guibas arXiv preprint arXiv:2212.05011, 2022	11	2022

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